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b/Owkin_PyRadiomics_and_clinical_pipeline.ipynb |
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{ |
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"nbformat": 4, |
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"nbformat_minor": 0, |
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"metadata": { |
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"colab": { |
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"name": "Owkin - PyRadiomics and clinical pipeline.ipynb", |
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"provenance": [], |
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"collapsed_sections": [], |
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"toc_visible": true |
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}, |
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"kernelspec": { |
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"name": "python3", |
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"display_name": "Python 3" |
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} |
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}, |
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"cells": [ |
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{ |
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"cell_type": "code", |
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"metadata": { |
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"id": "FpLNFVodLnzM", |
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"colab_type": "code", |
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"colab": {} |
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}, |
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"source": [ |
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"import os\n", |
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"os.chdir('./drive/My Drive/Challenge-Owkin/')" |
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], |
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"execution_count": 0, |
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"outputs": [] |
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}, |
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{ |
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"cell_type": "code", |
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"metadata": { |
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"id": "ZKtjbnNFJnoW", |
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"colab_type": "code", |
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"colab": {} |
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}, |
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"source": [ |
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"import pandas as pd\n", |
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"import numpy as np\n", |
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"import matplotlib.pyplot as plt" |
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], |
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"execution_count": 0, |
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"outputs": [] |
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}, |
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{ |
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"cell_type": "markdown", |
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"metadata": { |
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"id": "fTo18WgtdtLv", |
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"colab_type": "text" |
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}, |
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"source": [ |
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"# Metrics" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"metadata": { |
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"id": "czN1DE7odL-u", |
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"colab_type": "code", |
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"colab": {} |
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}, |
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"source": [ |
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"import numpy as np\n", |
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"import pandas as pd\n", |
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"import warnings\n", |
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"\n", |
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"\n", |
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"def cindex(y_true_times, predicted_times, tol=1e-8):\n", |
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" \"\"\"\n", |
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" Author: Romuald Menuet & Rémy Dubois\n", |
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"\n", |
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" Evaluate concordance index from Pandas DataFrame, taking ties into account.\n", |
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"\n", |
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" Args:\n", |
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" y_true_times: pd.DataFrame\n", |
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" pd DataFrame with three columns: `PatientID`, `Event` and `SurvivalTime` the float-valued column of true survival times.\n", |
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" predicted_times: pd.DataFrame\n", |
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" pd DataFrame with three columns: `PatientID`, `SurvivalTime` the float-valued column of predicted survival times,\n", |
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" and one `Event`column, whose value does not matter. It must be appended so that target and predictions have the same format.\n", |
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" tol: float\n", |
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" small float value for numerical stability.\n", |
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" Returns:\n", |
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" Concordance index, as described here:\n", |
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" https://square.github.io/pysurvival/metrics/c_index.html\n", |
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" \"\"\"\n", |
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"\n", |
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" assert isinstance(y_true_times, pd.DataFrame), 'Y true times should be pd dataframe with `PatientID` as index, and `Event` and `SurvivalTime` as columns'\n", |
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" assert isinstance(predicted_times, pd.DataFrame), 'Predicted times should be pd dataframe with patient `PatientID` as index, and `Event` and `SurvivalTime` as columns'\n", |
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" assert len(y_true_times.shape) == 2, 'Y true times should be pd dataframe with `PatientID` as index, and `Event` and `SurvivalTime` as columns'\n", |
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" assert len(predicted_times.shape) == 2, 'Predicted times should be pd dataframe with `PatientID` as index, and `Event` and `SurvivalTime` as columns'\n", |
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" assert set(y_true_times.columns) == {'Event', 'SurvivalTime'}, 'Y true times should be pd dataframe with `PatientID` as index, and `Event` and `SurvivalTime` as columns'\n", |
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" assert set(predicted_times.columns) == {'Event', 'SurvivalTime'}, 'Predicted times should be pd dataframe with `PatientID` as index, and `Event` and `SurvivalTime` as columns'\n", |
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" np.testing.assert_equal(y_true_times.shape, predicted_times.shape, err_msg=\"Not same amount of predicted versus true samples\")\n", |
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" assert set(y_true_times.index) == set(predicted_times.index), 'Not same patients in prediction versus ground truth'\n", |
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" assert np.all(predicted_times['SurvivalTime'] > 0), 'Predicted times should all be positive'\n", |
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"\n", |
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" events = y_true_times.Event\n", |
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" y_true_times = y_true_times.SurvivalTime\n", |
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" predicted_times = predicted_times.SurvivalTime\n", |
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"\n", |
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" # Just ordering the right way\n", |
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" predicted_times = predicted_times.loc[y_true_times.index]\n", |
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" events = events.loc[y_true_times.index]\n", |
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"\n", |
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" events = events.values.astype(int)\n", |
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" y_true_times = y_true_times.values.astype(float)\n", |
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" predicted_times = predicted_times.values.astype(float)\n", |
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" # events = events.values.astype(bool)\n", |
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"\n", |
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" np.testing.assert_array_less(1.,\n", |
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" predicted_times.astype(float),\n", |
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" err_msg=\"Predicted y_true_times all below 1.\\\n", |
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" It should be in days. Make sure that you are not predicting risk instead of time.\")\n", |
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"\n", |
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" return _cindex_np(y_true_times, predicted_times, events)\n", |
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"\n", |
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"\n", |
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"def _cindex_np(times, predicted_times, events, tol=1.e-8):\n", |
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" \"\"\"\n", |
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" Raw CI computation from np arrray. Should not be used as is.\n", |
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" \"\"\"\n", |
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" assert times.ndim == predicted_times.ndim == events.ndim == 1, \"wrong input, should be vectors only\"\n", |
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" assert times.shape[0] == predicted_times.shape[0] == events.shape[0], \"wrong input, should be vectors of the same len\"\n", |
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"\n", |
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" risks = - predicted_times\n", |
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"\n", |
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" risks_i = risks.reshape((-1, 1))\n", |
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" risks_j = risks.reshape((1, -1))\n", |
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" times_i = times.reshape((-1, 1))\n", |
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" times_j = times.reshape((1, -1))\n", |
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" events_i = events.reshape((-1, 1))\n", |
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"\n", |
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" eligible_pairs = (times_i < times_j) * events_i\n", |
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"\n", |
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" well_ordered = np.sum(eligible_pairs * (risks_i > risks_j))\n", |
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" ties = + np.sum(eligible_pairs * 0.5 * (risks_i == risks_j))\n", |
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"\n", |
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" return (well_ordered + ties) / (eligible_pairs.sum() + tol)\n" |
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], |
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"execution_count": 0, |
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"outputs": [] |
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}, |
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{ |
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"cell_type": "markdown", |
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"metadata": { |
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"id": "FXzQe3YxJi7P", |
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"colab_type": "text" |
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}, |
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"source": [ |
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"# Read files" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"metadata": { |
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"id": "78TS7toUKizf", |
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"colab_type": "code", |
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"colab": {} |
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}, |
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"source": [ |
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"from sklearn import preprocessing\n", |
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"\n", |
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"def normalizing_input(x_train, x_test):\n", |
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" all_x = pd.concat([x_train, x_test])\n", |
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" x_val = all_x.values\n", |
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" min_max_scaler = preprocessing.MinMaxScaler()\n", |
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" x_scaled = min_max_scaler.fit_transform(x_val)\n", |
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" df = pd.DataFrame(x_scaled, index=all_x.index, columns=all_x.columns)\n", |
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" return df.loc[x_train.index], df.loc[x_test.index]\n", |
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"\n", |
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"def cleaning_clinical(clinical):\n", |
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" \"\"\"\n", |
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" Cleaning the clinical dataframe.\n", |
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" :param clinical:\n", |
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" :return:\n", |
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" \"\"\"\n", |
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" # Encoding label for SourceDataset\n", |
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" le = preprocessing.LabelEncoder()\n", |
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" le.fit(clinical['SourceDataset'])\n", |
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" le.transform(clinical['SourceDataset'])\n", |
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" clinical['SourceDataset'] = le.transform(clinical['SourceDataset'])\n", |
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" # Cleaning Histology\n", |
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" clinical.replace(\"NSCLC NOS (not otherwise specified)\", \"nos\", inplace=True)\n", |
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" clinical.replace(\"Adenocarcinoma\", \"adenocarcinoma\", inplace=True)\n", |
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" clinical.replace(\"Squamous cell carcinoma\", \"squamous cell carcinoma\", inplace=True)\n", |
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" # Dummies for Histology\n", |
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" clinical = pd.get_dummies(clinical)\n", |
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" # Fill age nan\n", |
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" clinical['age'].fillna((clinical['age'].mean()), inplace=True)\n", |
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"\n", |
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" return clinical\n", |
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"\n", |
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"def clean_clinical_data(file, newfile):\n", |
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" \"\"\"\n", |
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" Cleaning clinical data.\n", |
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" :param file: clinical data file\n", |
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" :param newfile: path for the cleaned data file.\n", |
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" :return:\n", |
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" \"\"\"\n", |
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" clinical = pd.read_csv(file, index_col=0)\n", |
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" cleaned = cleaning_clinical(clinical)\n", |
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" cleaned.to_csv(newfile)\n", |
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"\n", |
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"def y_dataframe_to_rsf_input(y_df):\n", |
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" \"\"\"\n", |
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" Input for random survival forest.\n", |
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" :param y_df: event + survival time dataframe.\n", |
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" :return:\n", |
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" \"\"\"\n", |
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" y_array = []\n", |
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" Y = y_df.to_numpy()\n", |
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" for y in Y:\n", |
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" tuple = (bool(y[1]), y[0])\n", |
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" y_array.append(tuple)\n", |
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" return np.array(y_array, dtype = [(f'{y_df.columns[1]}', np.bool), (f'{y_df.columns[0]}', np.float)])" |
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], |
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"execution_count": 0, |
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"outputs": [] |
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}, |
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{ |
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"cell_type": "code", |
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"metadata": { |
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"id": "KdL8k83uHmrT", |
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"colab_type": "code", |
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"colab": {} |
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}, |
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"source": [ |
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"def read_input(file_radiomics, file_clinical):\n", |
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" \"\"\"\n", |
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" Read radiomics and clinical feature and return dataframe.\n", |
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" :param file_radiomics: filename\n", |
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" :param file_clinical: filename\n", |
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" :return:\n", |
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" \"\"\"\n", |
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" radiomics = pd.read_csv(file_radiomics, index_col=0)\n", |
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" clinical = pd.read_csv(file_clinical, index_col=0)\n", |
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" clinical = cleaning_clinical(clinical)\n", |
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" input = pd.concat([radiomics, clinical], axis=1)\n", |
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" return input\n", |
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"\n", |
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"def read_output(file_output):\n", |
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" \"\"\"\n", |
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" Return dataframe for event + survival time.\n", |
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" :param file_output:\n", |
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" :return:\n", |
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" \"\"\"\n", |
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" output = pd.read_csv(file_output, index_col=0, header=0)\n", |
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" return output\n", |
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"\n", |
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"def load_owkin_data(radiomics_path_train=\"data/train/features/radiomics.csv\",\n", |
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" clinical_path_train=\"data/train/features/clinical_data.csv\",\n", |
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" label_path_train='data/train/y_train.csv',\n", |
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" radiomics_path_test=\"data/test/features/radiomics.csv\",\n", |
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" clinical_path_test=\"data/test/features/clinical_data.csv\"):\n", |
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" \"\"\"\n", |
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" Load Owkin data: return PyRadiomics + clinical features of training set in dataframe,\n", |
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" event + time of training set in dataframe, and\n", |
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" PyRadiomics + clinical features of testing set in dataframe,\n", |
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" \"\"\"\n", |
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" input_train = read_input(radiomics_path_train, clinical_path_train)\n", |
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" output_train = read_output(label_path_train)\n", |
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" input_test = read_input(radiomics_path_test, clinical_path_test)\n", |
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" return input_train, output_train, input_test\n", |
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"\n", |
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"\n", |
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"input_train, output_train, input_test = load_owkin_data()\n", |
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"all_features = input_train.columns" |
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], |
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"execution_count": 0, |
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"outputs": [] |
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}, |
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{ |
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"cell_type": "markdown", |
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"metadata": { |
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"id": "NsBunJa1_aj-", |
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"colab_type": "text" |
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}, |
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"source": [ |
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|
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"# Data exploration" |
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] |
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}, |
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{ |
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"cell_type": "markdown", |
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"metadata": { |
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"id": "IdgtOIZ9ptQE", |
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"colab_type": "text" |
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}, |
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"source": [ |
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"## Global distribution" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"metadata": { |
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"id": "HhxnK1L-pvMB", |
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"colab_type": "code", |
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"outputId": "5ba5a029-0716-4b04-a34e-bb1c700d52a8", |
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"colab": { |
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"base_uri": "https://localhost:8080/", |
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"height": 487 |
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} |
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}, |
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"source": [ |
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"x_train, y_train, x_test = load_owkin_data()\n", |
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"x_train" |
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], |
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"execution_count": 0, |
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"outputs": [ |
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{ |
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"output_type": "execute_result", |
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"data": { |
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"text/html": [ |
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"<div>\n", |
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"<style scoped>\n", |
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" .dataframe tbody tr th:only-of-type {\n", |
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" vertical-align: middle;\n", |
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" <tr style=\"text-align: right;\">\n", |
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" <th></th>\n", |
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" <th>original_shape_Compactness1</th>\n", |
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" <th>original_shape_Compactness2</th>\n", |
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" <th>original_shape_Maximum3DDiameter</th>\n", |
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" <th>original_shape_SphericalDisproportion</th>\n", |
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" <th>original_shape_Sphericity</th>\n", |
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" <th>original_shape_SurfaceArea</th>\n", |
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" <th>original_shape_SurfaceVolumeRatio</th>\n", |
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" <th>original_shape_VoxelVolume</th>\n", |
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" <th>original_firstorder_Energy</th>\n", |
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" <th>original_firstorder_Entropy</th>\n", |
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" <th>original_firstorder_Kurtosis</th>\n", |
|
|
343 |
" <th>original_firstorder_Maximum</th>\n", |
|
|
344 |
" <th>original_firstorder_Mean</th>\n", |
|
|
345 |
" <th>original_firstorder_MeanAbsoluteDeviation</th>\n", |
|
|
346 |
" <th>original_firstorder_Median</th>\n", |
|
|
347 |
" <th>original_firstorder_Minimum</th>\n", |
|
|
348 |
" <th>original_firstorder_Range</th>\n", |
|
|
349 |
" <th>original_firstorder_RootMeanSquared</th>\n", |
|
|
350 |
" <th>original_firstorder_Skewness</th>\n", |
|
|
351 |
" <th>original_firstorder_StandardDeviation</th>\n", |
|
|
352 |
" <th>original_firstorder_Uniformity</th>\n", |
|
|
353 |
" <th>original_firstorder_Variance</th>\n", |
|
|
354 |
" <th>original_glcm_Autocorrelation</th>\n", |
|
|
355 |
" <th>original_glcm_ClusterProminence</th>\n", |
|
|
356 |
" <th>original_glcm_ClusterShade</th>\n", |
|
|
357 |
" <th>original_glcm_ClusterTendency</th>\n", |
|
|
358 |
" <th>original_glcm_Contrast</th>\n", |
|
|
359 |
" <th>original_glcm_Correlation</th>\n", |
|
|
360 |
" <th>original_glcm_DifferenceEntropy</th>\n", |
|
|
361 |
" <th>original_glcm_DifferenceAverage</th>\n", |
|
|
362 |
" <th>original_glcm_JointEnergy</th>\n", |
|
|
363 |
" <th>original_glcm_JointEntropy</th>\n", |
|
|
364 |
" <th>original_glcm_Id</th>\n", |
|
|
365 |
" <th>original_glcm_Idm</th>\n", |
|
|
366 |
" <th>original_glcm_Imc1</th>\n", |
|
|
367 |
" <th>original_glcm_Imc2</th>\n", |
|
|
368 |
" <th>original_glcm_Idmn</th>\n", |
|
|
369 |
" <th>original_glcm_Idn</th>\n", |
|
|
370 |
" <th>original_glcm_InverseVariance</th>\n", |
|
|
371 |
" <th>original_glcm_MaximumProbability</th>\n", |
|
|
372 |
" <th>original_glcm_SumAverage</th>\n", |
|
|
373 |
" <th>original_glcm_SumEntropy</th>\n", |
|
|
374 |
" <th>original_glrlm_ShortRunEmphasis</th>\n", |
|
|
375 |
" <th>original_glrlm_LongRunEmphasis</th>\n", |
|
|
376 |
" <th>original_glrlm_GrayLevelNonUniformity</th>\n", |
|
|
377 |
" <th>original_glrlm_RunLengthNonUniformity</th>\n", |
|
|
378 |
" <th>original_glrlm_RunPercentage</th>\n", |
|
|
379 |
" <th>original_glrlm_LowGrayLevelRunEmphasis</th>\n", |
|
|
380 |
" <th>original_glrlm_HighGrayLevelRunEmphasis</th>\n", |
|
|
381 |
" <th>original_glrlm_ShortRunLowGrayLevelEmphasis</th>\n", |
|
|
382 |
" <th>original_glrlm_ShortRunHighGrayLevelEmphasis</th>\n", |
|
|
383 |
" <th>original_glrlm_LongRunLowGrayLevelEmphasis</th>\n", |
|
|
384 |
" <th>original_glrlm_LongRunHighGrayLevelEmphasis</th>\n", |
|
|
385 |
" <th>Mstage</th>\n", |
|
|
386 |
" <th>Nstage</th>\n", |
|
|
387 |
" <th>SourceDataset</th>\n", |
|
|
388 |
" <th>Tstage</th>\n", |
|
|
389 |
" <th>age</th>\n", |
|
|
390 |
" <th>Histology_adenocarcinoma</th>\n", |
|
|
391 |
" <th>Histology_large cell</th>\n", |
|
|
392 |
" <th>Histology_nos</th>\n", |
|
|
393 |
" <th>Histology_squamous cell carcinoma</th>\n", |
|
|
394 |
" </tr>\n", |
|
|
395 |
" <tr>\n", |
|
|
396 |
" <th>PatientID</th>\n", |
|
|
397 |
" <th></th>\n", |
|
|
398 |
" <th></th>\n", |
|
|
399 |
" <th></th>\n", |
|
|
400 |
" <th></th>\n", |
|
|
401 |
" <th></th>\n", |
|
|
402 |
" <th></th>\n", |
|
|
403 |
" <th></th>\n", |
|
|
404 |
" <th></th>\n", |
|
|
405 |
" <th></th>\n", |
|
|
406 |
" <th></th>\n", |
|
|
407 |
" <th></th>\n", |
|
|
408 |
" <th></th>\n", |
|
|
409 |
" <th></th>\n", |
|
|
410 |
" <th></th>\n", |
|
|
411 |
" <th></th>\n", |
|
|
412 |
" <th></th>\n", |
|
|
413 |
" <th></th>\n", |
|
|
414 |
" <th></th>\n", |
|
|
415 |
" <th></th>\n", |
|
|
416 |
" <th></th>\n", |
|
|
417 |
" <th></th>\n", |
|
|
418 |
" <th></th>\n", |
|
|
419 |
" <th></th>\n", |
|
|
420 |
" <th></th>\n", |
|
|
421 |
" <th></th>\n", |
|
|
422 |
" <th></th>\n", |
|
|
423 |
" <th></th>\n", |
|
|
424 |
" <th></th>\n", |
|
|
425 |
" <th></th>\n", |
|
|
426 |
" <th></th>\n", |
|
|
427 |
" <th></th>\n", |
|
|
428 |
" <th></th>\n", |
|
|
429 |
" <th></th>\n", |
|
|
430 |
" <th></th>\n", |
|
|
431 |
" <th></th>\n", |
|
|
432 |
" <th></th>\n", |
|
|
433 |
" <th></th>\n", |
|
|
434 |
" <th></th>\n", |
|
|
435 |
" <th></th>\n", |
|
|
436 |
" <th></th>\n", |
|
|
437 |
" <th></th>\n", |
|
|
438 |
" <th></th>\n", |
|
|
439 |
" <th></th>\n", |
|
|
440 |
" <th></th>\n", |
|
|
441 |
" <th></th>\n", |
|
|
442 |
" <th></th>\n", |
|
|
443 |
" <th></th>\n", |
|
|
444 |
" <th></th>\n", |
|
|
445 |
" <th></th>\n", |
|
|
446 |
" <th></th>\n", |
|
|
447 |
" <th></th>\n", |
|
|
448 |
" <th></th>\n", |
|
|
449 |
" <th></th>\n", |
|
|
450 |
" <th></th>\n", |
|
|
451 |
" <th></th>\n", |
|
|
452 |
" <th></th>\n", |
|
|
453 |
" <th></th>\n", |
|
|
454 |
" <th></th>\n", |
|
|
455 |
" <th></th>\n", |
|
|
456 |
" <th></th>\n", |
|
|
457 |
" <th></th>\n", |
|
|
458 |
" <th></th>\n", |
|
|
459 |
" </tr>\n", |
|
|
460 |
" </thead>\n", |
|
|
461 |
" <tbody>\n", |
|
|
462 |
" <tr>\n", |
|
|
463 |
" <th>202</th>\n", |
|
|
464 |
" <td>0.027815</td>\n", |
|
|
465 |
" <td>0.274892</td>\n", |
|
|
466 |
" <td>48.559242</td>\n", |
|
|
467 |
" <td>1.537964</td>\n", |
|
|
468 |
" <td>0.650210</td>\n", |
|
|
469 |
" <td>5431.333210</td>\n", |
|
|
470 |
" <td>0.275228</td>\n", |
|
|
471 |
" <td>19786.0</td>\n", |
|
|
472 |
" <td>3.942944e+09</td>\n", |
|
|
473 |
" <td>5.138062</td>\n", |
|
|
474 |
" <td>1.592466</td>\n", |
|
|
475 |
" <td>241.0</td>\n", |
|
|
476 |
" <td>-310.858031</td>\n", |
|
|
477 |
" <td>288.311105</td>\n", |
|
|
478 |
" <td>-266.0</td>\n", |
|
|
479 |
" <td>-1006.0</td>\n", |
|
|
480 |
" <td>1247.0</td>\n", |
|
|
481 |
" <td>446.407319</td>\n", |
|
|
482 |
" <td>-0.281714</td>\n", |
|
|
483 |
" <td>320.385361</td>\n", |
|
|
484 |
" <td>0.037008</td>\n", |
|
|
485 |
" <td>102646.779451</td>\n", |
|
|
486 |
" <td>1056.843506</td>\n", |
|
|
487 |
" <td>546253.499762</td>\n", |
|
|
488 |
" <td>-5334.563631</td>\n", |
|
|
489 |
" <td>555.818455</td>\n", |
|
|
490 |
" <td>54.336816</td>\n", |
|
|
491 |
" <td>0.821101</td>\n", |
|
|
492 |
" <td>3.845383</td>\n", |
|
|
493 |
" <td>5.036775</td>\n", |
|
|
494 |
" <td>0.005920</td>\n", |
|
|
495 |
" <td>9.270194</td>\n", |
|
|
496 |
" <td>0.338672</td>\n", |
|
|
497 |
" <td>0.262780</td>\n", |
|
|
498 |
" <td>-0.175185</td>\n", |
|
|
499 |
" <td>0.901525</td>\n", |
|
|
500 |
" <td>0.981053</td>\n", |
|
|
501 |
" <td>0.917509</td>\n", |
|
|
502 |
" <td>0.249416</td>\n", |
|
|
503 |
" <td>0.037768</td>\n", |
|
|
504 |
" <td>61.035662</td>\n", |
|
|
505 |
" <td>6.007130</td>\n", |
|
|
506 |
" <td>0.926391</td>\n", |
|
|
507 |
" <td>1.457980</td>\n", |
|
|
508 |
" <td>555.378594</td>\n", |
|
|
509 |
" <td>14592.303727</td>\n", |
|
|
510 |
" <td>0.891330</td>\n", |
|
|
511 |
" <td>0.003562</td>\n", |
|
|
512 |
" <td>954.749135</td>\n", |
|
|
513 |
" <td>0.003384</td>\n", |
|
|
514 |
" <td>851.987373</td>\n", |
|
|
515 |
" <td>0.004444</td>\n", |
|
|
516 |
" <td>1651.710761</td>\n", |
|
|
517 |
" <td>0</td>\n", |
|
|
518 |
" <td>0</td>\n", |
|
|
519 |
" <td>1</td>\n", |
|
|
520 |
" <td>2</td>\n", |
|
|
521 |
" <td>66.000000</td>\n", |
|
|
522 |
" <td>1</td>\n", |
|
|
523 |
" <td>0</td>\n", |
|
|
524 |
" <td>0</td>\n", |
|
|
525 |
" <td>0</td>\n", |
|
|
526 |
" </tr>\n", |
|
|
527 |
" <tr>\n", |
|
|
528 |
" <th>371</th>\n", |
|
|
529 |
" <td>0.023015</td>\n", |
|
|
530 |
" <td>0.188210</td>\n", |
|
|
531 |
" <td>75.703368</td>\n", |
|
|
532 |
" <td>1.744961</td>\n", |
|
|
533 |
" <td>0.573079</td>\n", |
|
|
534 |
" <td>10369.568729</td>\n", |
|
|
535 |
" <td>0.240727</td>\n", |
|
|
536 |
" <td>43168.0</td>\n", |
|
|
537 |
" <td>3.122890e+09</td>\n", |
|
|
538 |
" <td>4.461054</td>\n", |
|
|
539 |
" <td>3.371013</td>\n", |
|
|
540 |
" <td>262.0</td>\n", |
|
|
541 |
" <td>-134.429809</td>\n", |
|
|
542 |
" <td>191.395154</td>\n", |
|
|
543 |
" <td>-15.0</td>\n", |
|
|
544 |
" <td>-945.0</td>\n", |
|
|
545 |
" <td>1207.0</td>\n", |
|
|
546 |
" <td>268.966000</td>\n", |
|
|
547 |
" <td>-1.216062</td>\n", |
|
|
548 |
" <td>232.962090</td>\n", |
|
|
549 |
" <td>0.080061</td>\n", |
|
|
550 |
" <td>54271.335557</td>\n", |
|
|
551 |
" <td>1219.689108</td>\n", |
|
|
552 |
" <td>331155.230396</td>\n", |
|
|
553 |
" <td>-6857.579704</td>\n", |
|
|
554 |
" <td>295.032309</td>\n", |
|
|
555 |
" <td>16.868781</td>\n", |
|
|
556 |
" <td>0.891184</td>\n", |
|
|
557 |
" <td>2.984034</td>\n", |
|
|
558 |
" <td>2.570017</td>\n", |
|
|
559 |
" <td>0.025532</td>\n", |
|
|
560 |
" <td>7.551168</td>\n", |
|
|
561 |
" <td>0.495719</td>\n", |
|
|
562 |
" <td>0.439879</td>\n", |
|
|
563 |
" <td>-0.250370</td>\n", |
|
|
564 |
" <td>0.932847</td>\n", |
|
|
565 |
" <td>0.993282</td>\n", |
|
|
566 |
" <td>0.953464</td>\n", |
|
|
567 |
" <td>0.340024</td>\n", |
|
|
568 |
" <td>0.110575</td>\n", |
|
|
569 |
" <td>67.826804</td>\n", |
|
|
570 |
" <td>5.255594</td>\n", |
|
|
571 |
" <td>0.855154</td>\n", |
|
|
572 |
" <td>2.512125</td>\n", |
|
|
573 |
" <td>1766.619373</td>\n", |
|
|
574 |
" <td>22881.932686</td>\n", |
|
|
575 |
" <td>0.766032</td>\n", |
|
|
576 |
" <td>0.002246</td>\n", |
|
|
577 |
" <td>1092.823724</td>\n", |
|
|
578 |
" <td>0.002069</td>\n", |
|
|
579 |
" <td>884.838627</td>\n", |
|
|
580 |
" <td>0.003633</td>\n", |
|
|
581 |
" <td>3386.995589</td>\n", |
|
|
582 |
" <td>0</td>\n", |
|
|
583 |
" <td>2</td>\n", |
|
|
584 |
" <td>0</td>\n", |
|
|
585 |
" <td>4</td>\n", |
|
|
586 |
" <td>64.572200</td>\n", |
|
|
587 |
" <td>0</td>\n", |
|
|
588 |
" <td>1</td>\n", |
|
|
589 |
" <td>0</td>\n", |
|
|
590 |
" <td>0</td>\n", |
|
|
591 |
" </tr>\n", |
|
|
592 |
" <tr>\n", |
|
|
593 |
" <th>246</th>\n", |
|
|
594 |
" <td>0.027348</td>\n", |
|
|
595 |
" <td>0.265740</td>\n", |
|
|
596 |
" <td>70.434367</td>\n", |
|
|
597 |
" <td>1.555420</td>\n", |
|
|
598 |
" <td>0.642913</td>\n", |
|
|
599 |
" <td>10558.818691</td>\n", |
|
|
600 |
" <td>0.200766</td>\n", |
|
|
601 |
" <td>52655.0</td>\n", |
|
|
602 |
" <td>1.059535e+09</td>\n", |
|
|
603 |
" <td>3.437111</td>\n", |
|
|
604 |
" <td>7.585494</td>\n", |
|
|
605 |
" <td>115.0</td>\n", |
|
|
606 |
" <td>-54.136093</td>\n", |
|
|
607 |
" <td>94.132136</td>\n", |
|
|
608 |
" <td>2.0</td>\n", |
|
|
609 |
" <td>-785.0</td>\n", |
|
|
610 |
" <td>900.0</td>\n", |
|
|
611 |
" <td>141.852795</td>\n", |
|
|
612 |
" <td>-2.153344</td>\n", |
|
|
613 |
" <td>131.116356</td>\n", |
|
|
614 |
" <td>0.160905</td>\n", |
|
|
615 |
" <td>17191.498865</td>\n", |
|
|
616 |
" <td>972.465798</td>\n", |
|
|
617 |
" <td>54523.086867</td>\n", |
|
|
618 |
" <td>-1618.363118</td>\n", |
|
|
619 |
" <td>78.183970</td>\n", |
|
|
620 |
" <td>7.040623</td>\n", |
|
|
621 |
" <td>0.831081</td>\n", |
|
|
622 |
" <td>2.333939</td>\n", |
|
|
623 |
" <td>1.496020</td>\n", |
|
|
624 |
" <td>0.066569</td>\n", |
|
|
625 |
" <td>5.686554</td>\n", |
|
|
626 |
" <td>0.616607</td>\n", |
|
|
627 |
" <td>0.581458</td>\n", |
|
|
628 |
" <td>-0.249969</td>\n", |
|
|
629 |
" <td>0.884911</td>\n", |
|
|
630 |
" <td>0.995095</td>\n", |
|
|
631 |
" <td>0.963887</td>\n", |
|
|
632 |
" <td>0.379957</td>\n", |
|
|
633 |
" <td>0.189723</td>\n", |
|
|
634 |
" <td>61.795144</td>\n", |
|
|
635 |
" <td>4.159019</td>\n", |
|
|
636 |
" <td>0.769265</td>\n", |
|
|
637 |
" <td>4.096838</td>\n", |
|
|
638 |
" <td>3462.816344</td>\n", |
|
|
639 |
" <td>18773.106002</td>\n", |
|
|
640 |
" <td>0.644554</td>\n", |
|
|
641 |
" <td>0.001880</td>\n", |
|
|
642 |
" <td>878.989601</td>\n", |
|
|
643 |
" <td>0.001635</td>\n", |
|
|
644 |
" <td>634.910036</td>\n", |
|
|
645 |
" <td>0.004869</td>\n", |
|
|
646 |
" <td>4232.418080</td>\n", |
|
|
647 |
" <td>0</td>\n", |
|
|
648 |
" <td>3</td>\n", |
|
|
649 |
" <td>0</td>\n", |
|
|
650 |
" <td>2</td>\n", |
|
|
651 |
" <td>66.045200</td>\n", |
|
|
652 |
" <td>0</td>\n", |
|
|
653 |
" <td>0</td>\n", |
|
|
654 |
" <td>0</td>\n", |
|
|
655 |
" <td>1</td>\n", |
|
|
656 |
" </tr>\n", |
|
|
657 |
" <tr>\n", |
|
|
658 |
" <th>240</th>\n", |
|
|
659 |
" <td>0.026811</td>\n", |
|
|
660 |
" <td>0.255406</td>\n", |
|
|
661 |
" <td>46.818800</td>\n", |
|
|
662 |
" <td>1.576120</td>\n", |
|
|
663 |
" <td>0.634469</td>\n", |
|
|
664 |
" <td>4221.412123</td>\n", |
|
|
665 |
" <td>0.323878</td>\n", |
|
|
666 |
" <td>13074.0</td>\n", |
|
|
667 |
" <td>6.583324e+08</td>\n", |
|
|
668 |
" <td>4.352564</td>\n", |
|
|
669 |
" <td>4.281865</td>\n", |
|
|
670 |
" <td>274.0</td>\n", |
|
|
671 |
" <td>-118.705522</td>\n", |
|
|
672 |
" <td>149.072998</td>\n", |
|
|
673 |
" <td>-37.0</td>\n", |
|
|
674 |
" <td>-850.0</td>\n", |
|
|
675 |
" <td>1124.0</td>\n", |
|
|
676 |
" <td>224.397694</td>\n", |
|
|
677 |
" <td>-1.437836</td>\n", |
|
|
678 |
" <td>190.429315</td>\n", |
|
|
679 |
" <td>0.074476</td>\n", |
|
|
680 |
" <td>36263.324022</td>\n", |
|
|
681 |
" <td>968.117567</td>\n", |
|
|
682 |
" <td>166735.779994</td>\n", |
|
|
683 |
" <td>-3968.921514</td>\n", |
|
|
684 |
" <td>184.299591</td>\n", |
|
|
685 |
" <td>13.656782</td>\n", |
|
|
686 |
" <td>0.860096</td>\n", |
|
|
687 |
" <td>2.926791</td>\n", |
|
|
688 |
" <td>2.492079</td>\n", |
|
|
689 |
" <td>0.015048</td>\n", |
|
|
690 |
" <td>7.536161</td>\n", |
|
|
691 |
" <td>0.457545</td>\n", |
|
|
692 |
" <td>0.391681</td>\n", |
|
|
693 |
" <td>-0.214233</td>\n", |
|
|
694 |
" <td>0.902811</td>\n", |
|
|
695 |
" <td>0.993509</td>\n", |
|
|
696 |
" <td>0.950374</td>\n", |
|
|
697 |
" <td>0.357236</td>\n", |
|
|
698 |
" <td>0.050439</td>\n", |
|
|
699 |
" <td>60.841486</td>\n", |
|
|
700 |
" <td>5.122729</td>\n", |
|
|
701 |
" <td>0.880123</td>\n", |
|
|
702 |
" <td>1.824328</td>\n", |
|
|
703 |
" <td>683.869792</td>\n", |
|
|
704 |
" <td>7978.285176</td>\n", |
|
|
705 |
" <td>0.828138</td>\n", |
|
|
706 |
" <td>0.002855</td>\n", |
|
|
707 |
" <td>907.045929</td>\n", |
|
|
708 |
" <td>0.002680</td>\n", |
|
|
709 |
" <td>774.885387</td>\n", |
|
|
710 |
" <td>0.003901</td>\n", |
|
|
711 |
" <td>1844.746046</td>\n", |
|
|
712 |
" <td>0</td>\n", |
|
|
713 |
" <td>2</td>\n", |
|
|
714 |
" <td>0</td>\n", |
|
|
715 |
" <td>3</td>\n", |
|
|
716 |
" <td>59.356600</td>\n", |
|
|
717 |
" <td>0</td>\n", |
|
|
718 |
" <td>0</td>\n", |
|
|
719 |
" <td>1</td>\n", |
|
|
720 |
" <td>0</td>\n", |
|
|
721 |
" </tr>\n", |
|
|
722 |
" <tr>\n", |
|
|
723 |
" <th>284</th>\n", |
|
|
724 |
" <td>0.023691</td>\n", |
|
|
725 |
" <td>0.199424</td>\n", |
|
|
726 |
" <td>53.795911</td>\n", |
|
|
727 |
" <td>1.711620</td>\n", |
|
|
728 |
" <td>0.584242</td>\n", |
|
|
729 |
" <td>5295.900331</td>\n", |
|
|
730 |
" <td>0.327241</td>\n", |
|
|
731 |
" <td>16237.0</td>\n", |
|
|
732 |
" <td>8.072532e+08</td>\n", |
|
|
733 |
" <td>4.055700</td>\n", |
|
|
734 |
" <td>8.999616</td>\n", |
|
|
735 |
" <td>203.0</td>\n", |
|
|
736 |
" <td>-67.201946</td>\n", |
|
|
737 |
" <td>143.210231</td>\n", |
|
|
738 |
" <td>14.0</td>\n", |
|
|
739 |
" <td>-1023.0</td>\n", |
|
|
740 |
" <td>1226.0</td>\n", |
|
|
741 |
" <td>222.972850</td>\n", |
|
|
742 |
" <td>-2.438490</td>\n", |
|
|
743 |
" <td>212.604774</td>\n", |
|
|
744 |
" <td>0.104419</td>\n", |
|
|
745 |
" <td>45200.790098</td>\n", |
|
|
746 |
" <td>1594.558416</td>\n", |
|
|
747 |
" <td>658805.553990</td>\n", |
|
|
748 |
" <td>-10509.915485</td>\n", |
|
|
749 |
" <td>248.373464</td>\n", |
|
|
750 |
" <td>20.121147</td>\n", |
|
|
751 |
" <td>0.849653</td>\n", |
|
|
752 |
" <td>2.787526</td>\n", |
|
|
753 |
" <td>2.372269</td>\n", |
|
|
754 |
" <td>0.029317</td>\n", |
|
|
755 |
" <td>6.940307</td>\n", |
|
|
756 |
" <td>0.544607</td>\n", |
|
|
757 |
" <td>0.501892</td>\n", |
|
|
758 |
" <td>-0.238174</td>\n", |
|
|
759 |
" <td>0.911356</td>\n", |
|
|
760 |
" <td>0.992658</td>\n", |
|
|
761 |
" <td>0.958908</td>\n", |
|
|
762 |
" <td>0.384513</td>\n", |
|
|
763 |
" <td>0.100536</td>\n", |
|
|
764 |
" <td>78.421376</td>\n", |
|
|
765 |
" <td>4.878253</td>\n", |
|
|
766 |
" <td>0.819049</td>\n", |
|
|
767 |
" <td>2.652580</td>\n", |
|
|
768 |
" <td>962.094760</td>\n", |
|
|
769 |
" <td>7520.054910</td>\n", |
|
|
770 |
" <td>0.734898</td>\n", |
|
|
771 |
" <td>0.006710</td>\n", |
|
|
772 |
" <td>1513.420445</td>\n", |
|
|
773 |
" <td>0.004704</td>\n", |
|
|
774 |
" <td>1197.140900</td>\n", |
|
|
775 |
" <td>0.023273</td>\n", |
|
|
776 |
" <td>4444.272812</td>\n", |
|
|
777 |
" <td>0</td>\n", |
|
|
778 |
" <td>3</td>\n", |
|
|
779 |
" <td>0</td>\n", |
|
|
780 |
" <td>4</td>\n", |
|
|
781 |
" <td>71.055400</td>\n", |
|
|
782 |
" <td>0</td>\n", |
|
|
783 |
" <td>0</td>\n", |
|
|
784 |
" <td>0</td>\n", |
|
|
785 |
" <td>1</td>\n", |
|
|
786 |
" </tr>\n", |
|
|
787 |
" <tr>\n", |
|
|
788 |
" <th>...</th>\n", |
|
|
789 |
" <td>...</td>\n", |
|
|
790 |
" <td>...</td>\n", |
|
|
791 |
" <td>...</td>\n", |
|
|
792 |
" <td>...</td>\n", |
|
|
793 |
" <td>...</td>\n", |
|
|
794 |
" <td>...</td>\n", |
|
|
795 |
" <td>...</td>\n", |
|
|
796 |
" <td>...</td>\n", |
|
|
797 |
" <td>...</td>\n", |
|
|
798 |
" <td>...</td>\n", |
|
|
799 |
" <td>...</td>\n", |
|
|
800 |
" <td>...</td>\n", |
|
|
801 |
" <td>...</td>\n", |
|
|
802 |
" <td>...</td>\n", |
|
|
803 |
" <td>...</td>\n", |
|
|
804 |
" <td>...</td>\n", |
|
|
805 |
" <td>...</td>\n", |
|
|
806 |
" <td>...</td>\n", |
|
|
807 |
" <td>...</td>\n", |
|
|
808 |
" <td>...</td>\n", |
|
|
809 |
" <td>...</td>\n", |
|
|
810 |
" <td>...</td>\n", |
|
|
811 |
" <td>...</td>\n", |
|
|
812 |
" <td>...</td>\n", |
|
|
813 |
" <td>...</td>\n", |
|
|
814 |
" <td>...</td>\n", |
|
|
815 |
" <td>...</td>\n", |
|
|
816 |
" <td>...</td>\n", |
|
|
817 |
" <td>...</td>\n", |
|
|
818 |
" <td>...</td>\n", |
|
|
819 |
" <td>...</td>\n", |
|
|
820 |
" <td>...</td>\n", |
|
|
821 |
" <td>...</td>\n", |
|
|
822 |
" <td>...</td>\n", |
|
|
823 |
" <td>...</td>\n", |
|
|
824 |
" <td>...</td>\n", |
|
|
825 |
" <td>...</td>\n", |
|
|
826 |
" <td>...</td>\n", |
|
|
827 |
" <td>...</td>\n", |
|
|
828 |
" <td>...</td>\n", |
|
|
829 |
" <td>...</td>\n", |
|
|
830 |
" <td>...</td>\n", |
|
|
831 |
" <td>...</td>\n", |
|
|
832 |
" <td>...</td>\n", |
|
|
833 |
" <td>...</td>\n", |
|
|
834 |
" <td>...</td>\n", |
|
|
835 |
" <td>...</td>\n", |
|
|
836 |
" <td>...</td>\n", |
|
|
837 |
" <td>...</td>\n", |
|
|
838 |
" <td>...</td>\n", |
|
|
839 |
" <td>...</td>\n", |
|
|
840 |
" <td>...</td>\n", |
|
|
841 |
" <td>...</td>\n", |
|
|
842 |
" <td>...</td>\n", |
|
|
843 |
" <td>...</td>\n", |
|
|
844 |
" <td>...</td>\n", |
|
|
845 |
" <td>...</td>\n", |
|
|
846 |
" <td>...</td>\n", |
|
|
847 |
" <td>...</td>\n", |
|
|
848 |
" <td>...</td>\n", |
|
|
849 |
" <td>...</td>\n", |
|
|
850 |
" <td>...</td>\n", |
|
|
851 |
" </tr>\n", |
|
|
852 |
" <tr>\n", |
|
|
853 |
" <th>261</th>\n", |
|
|
854 |
" <td>0.035752</td>\n", |
|
|
855 |
" <td>0.454151</td>\n", |
|
|
856 |
" <td>36.619667</td>\n", |
|
|
857 |
" <td>1.300968</td>\n", |
|
|
858 |
" <td>0.768658</td>\n", |
|
|
859 |
" <td>3143.972679</td>\n", |
|
|
860 |
" <td>0.281441</td>\n", |
|
|
861 |
" <td>11204.0</td>\n", |
|
|
862 |
" <td>1.299546e+09</td>\n", |
|
|
863 |
" <td>4.747964</td>\n", |
|
|
864 |
" <td>1.925799</td>\n", |
|
|
865 |
" <td>89.0</td>\n", |
|
|
866 |
" <td>-238.768476</td>\n", |
|
|
867 |
" <td>214.212310</td>\n", |
|
|
868 |
" <td>-178.5</td>\n", |
|
|
869 |
" <td>-901.0</td>\n", |
|
|
870 |
" <td>990.0</td>\n", |
|
|
871 |
" <td>340.572258</td>\n", |
|
|
872 |
" <td>-0.515713</td>\n", |
|
|
873 |
" <td>242.856085</td>\n", |
|
|
874 |
" <td>0.052870</td>\n", |
|
|
875 |
" <td>58979.078135</td>\n", |
|
|
876 |
" <td>924.461193</td>\n", |
|
|
877 |
" <td>227251.294990</td>\n", |
|
|
878 |
" <td>-3840.952856</td>\n", |
|
|
879 |
" <td>325.775953</td>\n", |
|
|
880 |
" <td>17.384511</td>\n", |
|
|
881 |
" <td>0.898046</td>\n", |
|
|
882 |
" <td>3.111318</td>\n", |
|
|
883 |
" <td>2.831998</td>\n", |
|
|
884 |
" <td>0.016200</td>\n", |
|
|
885 |
" <td>8.006776</td>\n", |
|
|
886 |
" <td>0.451176</td>\n", |
|
|
887 |
" <td>0.383987</td>\n", |
|
|
888 |
" <td>-0.271559</td>\n", |
|
|
889 |
" <td>0.953994</td>\n", |
|
|
890 |
" <td>0.990157</td>\n", |
|
|
891 |
" <td>0.939545</td>\n", |
|
|
892 |
" <td>0.306048</td>\n", |
|
|
893 |
" <td>0.095692</td>\n", |
|
|
894 |
" <td>58.216494</td>\n", |
|
|
895 |
" <td>5.587446</td>\n", |
|
|
896 |
" <td>0.885250</td>\n", |
|
|
897 |
" <td>2.126745</td>\n", |
|
|
898 |
" <td>336.573622</td>\n", |
|
|
899 |
" <td>6761.904881</td>\n", |
|
|
900 |
" <td>0.807157</td>\n", |
|
|
901 |
" <td>0.003026</td>\n", |
|
|
902 |
" <td>791.295506</td>\n", |
|
|
903 |
" <td>0.002829</td>\n", |
|
|
904 |
" <td>659.103280</td>\n", |
|
|
905 |
" <td>0.004400</td>\n", |
|
|
906 |
" <td>2243.689318</td>\n", |
|
|
907 |
" <td>0</td>\n", |
|
|
908 |
" <td>0</td>\n", |
|
|
909 |
" <td>0</td>\n", |
|
|
910 |
" <td>1</td>\n", |
|
|
911 |
" <td>87.126600</td>\n", |
|
|
912 |
" <td>0</td>\n", |
|
|
913 |
" <td>0</td>\n", |
|
|
914 |
" <td>0</td>\n", |
|
|
915 |
" <td>1</td>\n", |
|
|
916 |
" </tr>\n", |
|
|
917 |
" <tr>\n", |
|
|
918 |
" <th>298</th>\n", |
|
|
919 |
" <td>0.011765</td>\n", |
|
|
920 |
" <td>0.049182</td>\n", |
|
|
921 |
" <td>122.531629</td>\n", |
|
|
922 |
" <td>2.729392</td>\n", |
|
|
923 |
" <td>0.366382</td>\n", |
|
|
924 |
" <td>20785.949532</td>\n", |
|
|
925 |
" <td>0.332614</td>\n", |
|
|
926 |
" <td>62744.0</td>\n", |
|
|
927 |
" <td>3.952856e+08</td>\n", |
|
|
928 |
" <td>2.716180</td>\n", |
|
|
929 |
" <td>57.125568</td>\n", |
|
|
930 |
" <td>843.0</td>\n", |
|
|
931 |
" <td>10.986086</td>\n", |
|
|
932 |
" <td>35.924670</td>\n", |
|
|
933 |
" <td>21.0</td>\n", |
|
|
934 |
" <td>-971.0</td>\n", |
|
|
935 |
" <td>1814.0</td>\n", |
|
|
936 |
" <td>79.372376</td>\n", |
|
|
937 |
" <td>-5.466666</td>\n", |
|
|
938 |
" <td>78.608396</td>\n", |
|
|
939 |
" <td>0.225383</td>\n", |
|
|
940 |
" <td>6179.279913</td>\n", |
|
|
941 |
" <td>1612.770951</td>\n", |
|
|
942 |
" <td>56349.565987</td>\n", |
|
|
943 |
" <td>-887.363706</td>\n", |
|
|
944 |
" <td>28.577221</td>\n", |
|
|
945 |
" <td>3.267889</td>\n", |
|
|
946 |
" <td>0.792003</td>\n", |
|
|
947 |
" <td>1.723912</td>\n", |
|
|
948 |
" <td>0.905901</td>\n", |
|
|
949 |
" <td>0.086325</td>\n", |
|
|
950 |
" <td>4.629383</td>\n", |
|
|
951 |
" <td>0.684890</td>\n", |
|
|
952 |
" <td>0.666367</td>\n", |
|
|
953 |
" <td>-0.227001</td>\n", |
|
|
954 |
" <td>0.818529</td>\n", |
|
|
955 |
" <td>0.999401</td>\n", |
|
|
956 |
" <td>0.988139</td>\n", |
|
|
957 |
" <td>0.450041</td>\n", |
|
|
958 |
" <td>0.170998</td>\n", |
|
|
959 |
" <td>80.160908</td>\n", |
|
|
960 |
" <td>3.474101</td>\n", |
|
|
961 |
" <td>0.711658</td>\n", |
|
|
962 |
" <td>4.405809</td>\n", |
|
|
963 |
" <td>7158.438787</td>\n", |
|
|
964 |
" <td>18591.140071</td>\n", |
|
|
965 |
" <td>0.613336</td>\n", |
|
|
966 |
" <td>0.001022</td>\n", |
|
|
967 |
" <td>1592.190051</td>\n", |
|
|
968 |
" <td>0.000812</td>\n", |
|
|
969 |
" <td>1123.462320</td>\n", |
|
|
970 |
" <td>0.003265</td>\n", |
|
|
971 |
" <td>7145.459719</td>\n", |
|
|
972 |
" <td>0</td>\n", |
|
|
973 |
" <td>0</td>\n", |
|
|
974 |
" <td>0</td>\n", |
|
|
975 |
" <td>1</td>\n", |
|
|
976 |
" <td>68.766856</td>\n", |
|
|
977 |
" <td>0</td>\n", |
|
|
978 |
" <td>0</td>\n", |
|
|
979 |
" <td>0</td>\n", |
|
|
980 |
" <td>1</td>\n", |
|
|
981 |
" </tr>\n", |
|
|
982 |
" <tr>\n", |
|
|
983 |
" <th>129</th>\n", |
|
|
984 |
" <td>0.025668</td>\n", |
|
|
985 |
" <td>0.234096</td>\n", |
|
|
986 |
" <td>64.876806</td>\n", |
|
|
987 |
" <td>1.622565</td>\n", |
|
|
988 |
" <td>0.616308</td>\n", |
|
|
989 |
" <td>9571.675020</td>\n", |
|
|
990 |
" <td>0.224665</td>\n", |
|
|
991 |
" <td>42663.0</td>\n", |
|
|
992 |
" <td>1.227827e+08</td>\n", |
|
|
993 |
" <td>2.368997</td>\n", |
|
|
994 |
" <td>51.791276</td>\n", |
|
|
995 |
" <td>449.0</td>\n", |
|
|
996 |
" <td>20.628577</td>\n", |
|
|
997 |
" <td>27.675471</td>\n", |
|
|
998 |
" <td>31.0</td>\n", |
|
|
999 |
" <td>-751.0</td>\n", |
|
|
1000 |
" <td>1200.0</td>\n", |
|
|
1001 |
" <td>53.646685</td>\n", |
|
|
1002 |
" <td>-5.135133</td>\n", |
|
|
1003 |
" <td>49.522001</td>\n", |
|
|
1004 |
" <td>0.275115</td>\n", |
|
|
1005 |
" <td>2452.428625</td>\n", |
|
|
1006 |
" <td>1055.791154</td>\n", |
|
|
1007 |
" <td>6889.281619</td>\n", |
|
|
1008 |
" <td>-186.782360</td>\n", |
|
|
1009 |
" <td>10.944156</td>\n", |
|
|
1010 |
" <td>1.439822</td>\n", |
|
|
1011 |
" <td>0.762876</td>\n", |
|
|
1012 |
" <td>1.440679</td>\n", |
|
|
1013 |
" <td>0.633557</td>\n", |
|
|
1014 |
" <td>0.130721</td>\n", |
|
|
1015 |
" <td>3.939592</td>\n", |
|
|
1016 |
" <td>0.746369</td>\n", |
|
|
1017 |
" <td>0.735862</td>\n", |
|
|
1018 |
" <td>-0.264988</td>\n", |
|
|
1019 |
" <td>0.824795</td>\n", |
|
|
1020 |
" <td>0.999412</td>\n", |
|
|
1021 |
" <td>0.987616</td>\n", |
|
|
1022 |
" <td>0.408032</td>\n", |
|
|
1023 |
" <td>0.295579</td>\n", |
|
|
1024 |
" <td>64.912687</td>\n", |
|
|
1025 |
" <td>3.116713</td>\n", |
|
|
1026 |
" <td>0.631183</td>\n", |
|
|
1027 |
" <td>7.254939</td>\n", |
|
|
1028 |
" <td>4513.100699</td>\n", |
|
|
1029 |
" <td>8467.466888</td>\n", |
|
|
1030 |
" <td>0.511173</td>\n", |
|
|
1031 |
" <td>0.001135</td>\n", |
|
|
1032 |
" <td>1030.197953</td>\n", |
|
|
1033 |
" <td>0.000784</td>\n", |
|
|
1034 |
" <td>637.718544</td>\n", |
|
|
1035 |
" <td>0.006983</td>\n", |
|
|
1036 |
" <td>7752.599509</td>\n", |
|
|
1037 |
" <td>0</td>\n", |
|
|
1038 |
" <td>1</td>\n", |
|
|
1039 |
" <td>0</td>\n", |
|
|
1040 |
" <td>3</td>\n", |
|
|
1041 |
" <td>59.843900</td>\n", |
|
|
1042 |
" <td>0</td>\n", |
|
|
1043 |
" <td>1</td>\n", |
|
|
1044 |
" <td>0</td>\n", |
|
|
1045 |
" <td>0</td>\n", |
|
|
1046 |
" </tr>\n", |
|
|
1047 |
" <tr>\n", |
|
|
1048 |
" <th>273</th>\n", |
|
|
1049 |
" <td>0.032517</td>\n", |
|
|
1050 |
" <td>0.375695</td>\n", |
|
|
1051 |
" <td>22.472205</td>\n", |
|
|
1052 |
" <td>1.385866</td>\n", |
|
|
1053 |
" <td>0.721570</td>\n", |
|
|
1054 |
" <td>820.276370</td>\n", |
|
|
1055 |
" <td>0.605798</td>\n", |
|
|
1056 |
" <td>1377.0</td>\n", |
|
|
1057 |
" <td>4.253586e+08</td>\n", |
|
|
1058 |
" <td>5.129264</td>\n", |
|
|
1059 |
" <td>1.585915</td>\n", |
|
|
1060 |
" <td>68.0</td>\n", |
|
|
1061 |
" <td>-464.367466</td>\n", |
|
|
1062 |
" <td>273.935074</td>\n", |
|
|
1063 |
" <td>-525.0</td>\n", |
|
|
1064 |
" <td>-948.0</td>\n", |
|
|
1065 |
" <td>1016.0</td>\n", |
|
|
1066 |
" <td>555.789862</td>\n", |
|
|
1067 |
" <td>0.281740</td>\n", |
|
|
1068 |
" <td>305.393561</td>\n", |
|
|
1069 |
" <td>0.032073</td>\n", |
|
|
1070 |
" <td>93265.227351</td>\n", |
|
|
1071 |
" <td>614.128952</td>\n", |
|
|
1072 |
" <td>407492.165534</td>\n", |
|
|
1073 |
" <td>266.873157</td>\n", |
|
|
1074 |
" <td>497.132773</td>\n", |
|
|
1075 |
" <td>78.081431</td>\n", |
|
|
1076 |
" <td>0.728237</td>\n", |
|
|
1077 |
" <td>4.126944</td>\n", |
|
|
1078 |
" <td>6.580868</td>\n", |
|
|
1079 |
" <td>0.002750</td>\n", |
|
|
1080 |
" <td>9.201181</td>\n", |
|
|
1081 |
" <td>0.272701</td>\n", |
|
|
1082 |
" <td>0.192854</td>\n", |
|
|
1083 |
" <td>-0.212124</td>\n", |
|
|
1084 |
" <td>0.939127</td>\n", |
|
|
1085 |
" <td>0.959449</td>\n", |
|
|
1086 |
" <td>0.873813</td>\n", |
|
|
1087 |
" <td>0.190427</td>\n", |
|
|
1088 |
" <td>0.016097</td>\n", |
|
|
1089 |
" <td>45.121191</td>\n", |
|
|
1090 |
" <td>6.104251</td>\n", |
|
|
1091 |
" <td>0.952612</td>\n", |
|
|
1092 |
" <td>1.231149</td>\n", |
|
|
1093 |
" <td>40.140124</td>\n", |
|
|
1094 |
" <td>1137.721790</td>\n", |
|
|
1095 |
" <td>0.934473</td>\n", |
|
|
1096 |
" <td>0.013537</td>\n", |
|
|
1097 |
" <td>525.682829</td>\n", |
|
|
1098 |
" <td>0.012984</td>\n", |
|
|
1099 |
" <td>486.598834</td>\n", |
|
|
1100 |
" <td>0.016086</td>\n", |
|
|
1101 |
" <td>726.945564</td>\n", |
|
|
1102 |
" <td>0</td>\n", |
|
|
1103 |
" <td>0</td>\n", |
|
|
1104 |
" <td>1</td>\n", |
|
|
1105 |
" <td>1</td>\n", |
|
|
1106 |
" <td>70.000000</td>\n", |
|
|
1107 |
" <td>1</td>\n", |
|
|
1108 |
" <td>0</td>\n", |
|
|
1109 |
" <td>0</td>\n", |
|
|
1110 |
" <td>0</td>\n", |
|
|
1111 |
" </tr>\n", |
|
|
1112 |
" <tr>\n", |
|
|
1113 |
" <th>366</th>\n", |
|
|
1114 |
" <td>0.031294</td>\n", |
|
|
1115 |
" <td>0.347965</td>\n", |
|
|
1116 |
" <td>67.542579</td>\n", |
|
|
1117 |
" <td>1.421744</td>\n", |
|
|
1118 |
" <td>0.703361</td>\n", |
|
|
1119 |
" <td>9620.491997</td>\n", |
|
|
1120 |
" <td>0.183806</td>\n", |
|
|
1121 |
" <td>52399.0</td>\n", |
|
|
1122 |
" <td>2.762241e+09</td>\n", |
|
|
1123 |
" <td>4.227597</td>\n", |
|
|
1124 |
" <td>3.987643</td>\n", |
|
|
1125 |
" <td>173.0</td>\n", |
|
|
1126 |
" <td>-104.654402</td>\n", |
|
|
1127 |
" <td>163.293001</td>\n", |
|
|
1128 |
" <td>-3.0</td>\n", |
|
|
1129 |
" <td>-951.0</td>\n", |
|
|
1130 |
" <td>1124.0</td>\n", |
|
|
1131 |
" <td>229.598607</td>\n", |
|
|
1132 |
" <td>-1.422339</td>\n", |
|
|
1133 |
" <td>204.359919</td>\n", |
|
|
1134 |
" <td>0.089917</td>\n", |
|
|
1135 |
" <td>41762.976327</td>\n", |
|
|
1136 |
" <td>1347.121905</td>\n", |
|
|
1137 |
" <td>231987.589044</td>\n", |
|
|
1138 |
" <td>-5333.113542</td>\n", |
|
|
1139 |
" <td>225.056577</td>\n", |
|
|
1140 |
" <td>12.072458</td>\n", |
|
|
1141 |
" <td>0.897295</td>\n", |
|
|
1142 |
" <td>2.801756</td>\n", |
|
|
1143 |
" <td>2.259543</td>\n", |
|
|
1144 |
" <td>0.022109</td>\n", |
|
|
1145 |
" <td>7.243873</td>\n", |
|
|
1146 |
" <td>0.484819</td>\n", |
|
|
1147 |
" <td>0.425104</td>\n", |
|
|
1148 |
" <td>-0.234808</td>\n", |
|
|
1149 |
" <td>0.914060</td>\n", |
|
|
1150 |
" <td>0.994511</td>\n", |
|
|
1151 |
" <td>0.955753</td>\n", |
|
|
1152 |
" <td>0.380151</td>\n", |
|
|
1153 |
" <td>0.071257</td>\n", |
|
|
1154 |
" <td>71.940366</td>\n", |
|
|
1155 |
" <td>4.987725</td>\n", |
|
|
1156 |
" <td>0.858965</td>\n", |
|
|
1157 |
" <td>2.085791</td>\n", |
|
|
1158 |
" <td>2982.109496</td>\n", |
|
|
1159 |
" <td>29229.001918</td>\n", |
|
|
1160 |
" <td>0.797732</td>\n", |
|
|
1161 |
" <td>0.001272</td>\n", |
|
|
1162 |
" <td>1258.994044</td>\n", |
|
|
1163 |
" <td>0.001148</td>\n", |
|
|
1164 |
" <td>1046.829951</td>\n", |
|
|
1165 |
" <td>0.002128</td>\n", |
|
|
1166 |
" <td>2947.807968</td>\n", |
|
|
1167 |
" <td>0</td>\n", |
|
|
1168 |
" <td>3</td>\n", |
|
|
1169 |
" <td>0</td>\n", |
|
|
1170 |
" <td>2</td>\n", |
|
|
1171 |
" <td>54.694000</td>\n", |
|
|
1172 |
" <td>0</td>\n", |
|
|
1173 |
" <td>1</td>\n", |
|
|
1174 |
" <td>0</td>\n", |
|
|
1175 |
" <td>0</td>\n", |
|
|
1176 |
" </tr>\n", |
|
|
1177 |
" </tbody>\n", |
|
|
1178 |
"</table>\n", |
|
|
1179 |
"<p>300 rows × 62 columns</p>\n", |
|
|
1180 |
"</div>" |
|
|
1181 |
], |
|
|
1182 |
"text/plain": [ |
|
|
1183 |
" original_shape_Compactness1 ... Histology_squamous cell carcinoma\n", |
|
|
1184 |
"PatientID ... \n", |
|
|
1185 |
"202 0.027815 ... 0\n", |
|
|
1186 |
"371 0.023015 ... 0\n", |
|
|
1187 |
"246 0.027348 ... 1\n", |
|
|
1188 |
"240 0.026811 ... 0\n", |
|
|
1189 |
"284 0.023691 ... 1\n", |
|
|
1190 |
"... ... ... ...\n", |
|
|
1191 |
"261 0.035752 ... 1\n", |
|
|
1192 |
"298 0.011765 ... 1\n", |
|
|
1193 |
"129 0.025668 ... 0\n", |
|
|
1194 |
"273 0.032517 ... 0\n", |
|
|
1195 |
"366 0.031294 ... 0\n", |
|
|
1196 |
"\n", |
|
|
1197 |
"[300 rows x 62 columns]" |
|
|
1198 |
] |
|
|
1199 |
}, |
|
|
1200 |
"metadata": { |
|
|
1201 |
"tags": [] |
|
|
1202 |
}, |
|
|
1203 |
"execution_count": 6 |
|
|
1204 |
} |
|
|
1205 |
] |
|
|
1206 |
}, |
|
|
1207 |
{ |
|
|
1208 |
"cell_type": "markdown", |
|
|
1209 |
"metadata": { |
|
|
1210 |
"id": "73VhYwZnVwDO", |
|
|
1211 |
"colab_type": "text" |
|
|
1212 |
}, |
|
|
1213 |
"source": [ |
|
|
1214 |
"## Low and high survival probability population" |
|
|
1215 |
] |
|
|
1216 |
}, |
|
|
1217 |
{ |
|
|
1218 |
"cell_type": "markdown", |
|
|
1219 |
"metadata": { |
|
|
1220 |
"id": "vLJI7KyWV4dm", |
|
|
1221 |
"colab_type": "text" |
|
|
1222 |
}, |
|
|
1223 |
"source": [ |
|
|
1224 |
"En regardant le temps de survie dans patients labelisé Event = 0 ou Event = 1, j'ai splitté deux groupes de populations, une qui a une très faible chance de survie, et une autre avec une grand chance de survie, pour voir si ces deux groupes avaient des valeurs de features caractéristiques. Résultat: y'a juste deux features très discriminants, SourceDataset (ce qui est assez vache) et Histology_adenocarcinoma, et quelques autres qui discriminent un peu (les valeurs moyennes sont assez proches, mais la dispersion est encore petite pour pouvoir dire qu'on a deux groupes différents). Pour les autres, soit la dispersion est trop grande donc on peut rien dire, soit au contraire ces features se concentrent autour de la même valeur pour les deux populations, donc ça permet pas de différencier les populations. Le dataset en soi est donc assez difficile. Je pense que dans un premier temps faut tèje les variables inutiles (trop dispersées ou non informatives), ça sera la partie feature sélection, et après l'idéal c'est de construire des nouveaux features discriminants à partir du set restreint de features PyRadiomics / clinique, et images." |
|
|
1225 |
] |
|
|
1226 |
}, |
|
|
1227 |
{ |
|
|
1228 |
"cell_type": "code", |
|
|
1229 |
"metadata": { |
|
|
1230 |
"id": "C5eRHmFy_fn4", |
|
|
1231 |
"colab_type": "code", |
|
|
1232 |
"colab": {} |
|
|
1233 |
}, |
|
|
1234 |
"source": [ |
|
|
1235 |
"x_train, y_train, x_test = load_owkin_data()\n", |
|
|
1236 |
"x_train, x_test = normalizing_input(x_train, x_test)" |
|
|
1237 |
], |
|
|
1238 |
"execution_count": 0, |
|
|
1239 |
"outputs": [] |
|
|
1240 |
}, |
|
|
1241 |
{ |
|
|
1242 |
"cell_type": "code", |
|
|
1243 |
"metadata": { |
|
|
1244 |
"id": "98GdkDDUJBQ8", |
|
|
1245 |
"colab_type": "code", |
|
|
1246 |
"outputId": "1a6425ee-9aa6-424a-d744-5ed35172cf2b", |
|
|
1247 |
"colab": { |
|
|
1248 |
"base_uri": "https://localhost:8080/", |
|
|
1249 |
"height": 662 |
|
|
1250 |
} |
|
|
1251 |
}, |
|
|
1252 |
"source": [ |
|
|
1253 |
"\"\"\" Distribution of patient in event time \"\"\"\n", |
|
|
1254 |
"dead = y_train.loc[y_train['Event'] == 1]['SurvivalTime']\n", |
|
|
1255 |
"alive = y_train.loc[y_train['Event'] == 0]['SurvivalTime']\n", |
|
|
1256 |
"print(len(dead), len(alive))\n", |
|
|
1257 |
"\n", |
|
|
1258 |
"plt.figure(figsize=(12, 3))\n", |
|
|
1259 |
"plt.plot(dead, np.zeros(len(dead)), 'o', label='Event = 1')\n", |
|
|
1260 |
"plt.plot(alive, np.zeros(len(alive)) + 0.4, 'o', label='Event = 0')\n", |
|
|
1261 |
"plt.ylim([-0.5, 0.9])\n", |
|
|
1262 |
"plt.xlabel('Event time')\n", |
|
|
1263 |
"plt.yticks([])\n", |
|
|
1264 |
"plt.legend()\n", |
|
|
1265 |
"plt.title('Distribution in event time')\n", |
|
|
1266 |
"\n", |
|
|
1267 |
"plt.figure(figsize=(8,6))\n", |
|
|
1268 |
"n_bins = 50\n", |
|
|
1269 |
"plt.hist(alive, bins = n_bins, fc=(1, 0, 0, 0.5), label = 'Event = 0')\n", |
|
|
1270 |
"plt.hist(dead, bins = n_bins, fc=(0, 0, 1, 0.5), label = 'Event = 1')\n", |
|
|
1271 |
"plt.legend()\n", |
|
|
1272 |
"plt.xlabel('Event time')\n", |
|
|
1273 |
"plt.title('Histogram distribution according to event')\n" |
|
|
1274 |
], |
|
|
1275 |
"execution_count": 0, |
|
|
1276 |
"outputs": [ |
|
|
1277 |
{ |
|
|
1278 |
"output_type": "stream", |
|
|
1279 |
"text": [ |
|
|
1280 |
"162 138\n" |
|
|
1281 |
], |
|
|
1282 |
"name": "stdout" |
|
|
1283 |
}, |
|
|
1284 |
{ |
|
|
1285 |
"output_type": "execute_result", |
|
|
1286 |
"data": { |
|
|
1287 |
"text/plain": [ |
|
|
1288 |
"Text(0.5, 1.0, 'Histogram distribution according to event')" |
|
|
1289 |
] |
|
|
1290 |
}, |
|
|
1291 |
"metadata": { |
|
|
1292 |
"tags": [] |
|
|
1293 |
}, |
|
|
1294 |
"execution_count": 8 |
|
|
1295 |
}, |
|
|
1296 |
{ |
|
|
1297 |
"output_type": "display_data", |
|
|
1298 |
"data": { |
|
|
1299 |
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|
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1300 |
"text/plain": [ |
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1301 |
"<Figure size 864x216 with 1 Axes>" |
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1302 |
] |
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1303 |
}, |
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1304 |
"metadata": { |
|
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1305 |
"tags": [] |
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1306 |
} |
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1307 |
}, |
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1308 |
{ |
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1309 |
"output_type": "display_data", |
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1310 |
"data": { |
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1311 |
"image/png": 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uVh+3mwPz6+N52mQ4toEdgIV192zgl3V7OmqfDtPOjtqn9X6ZVXfPAK6t99NX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\nWJKkAgxgSZIK+P8mv0jx/1mtIQAAAABJRU5ErkJggg==\n", |
|
|
1312 |
"text/plain": [ |
|
|
1313 |
"<Figure size 576x432 with 1 Axes>" |
|
|
1314 |
] |
|
|
1315 |
}, |
|
|
1316 |
"metadata": { |
|
|
1317 |
"tags": [] |
|
|
1318 |
} |
|
|
1319 |
} |
|
|
1320 |
] |
|
|
1321 |
}, |
|
|
1322 |
{ |
|
|
1323 |
"cell_type": "markdown", |
|
|
1324 |
"metadata": { |
|
|
1325 |
"id": "ojlTTL1XOwgI", |
|
|
1326 |
"colab_type": "text" |
|
|
1327 |
}, |
|
|
1328 |
"source": [ |
|
|
1329 |
"**Hypothesis**: Patients with event 1 (people who died in the experience) with an event time less than 300 are considered as low survival probability patients. Patients with event 0 (people who are still alive in the experience) with an event time higher than 2000 are considered as high survival probability patients." |
|
|
1330 |
] |
|
|
1331 |
}, |
|
|
1332 |
{ |
|
|
1333 |
"cell_type": "code", |
|
|
1334 |
"metadata": { |
|
|
1335 |
"id": "vyKCOyKfOFB1", |
|
|
1336 |
"colab_type": "code", |
|
|
1337 |
"outputId": "030731e1-4686-4c38-87ce-6fa31e6a215e", |
|
|
1338 |
"colab": { |
|
|
1339 |
"base_uri": "https://localhost:8080/", |
|
|
1340 |
"height": 204 |
|
|
1341 |
} |
|
|
1342 |
}, |
|
|
1343 |
"source": [ |
|
|
1344 |
"\"\"\" Low survival probability patients \"\"\"\n", |
|
|
1345 |
"dead_df = y_train.loc[y_train['Event'] == 1] \n", |
|
|
1346 |
"low_survival_index = dead_df.loc[dead_df['SurvivalTime'] <= 500].index\n", |
|
|
1347 |
"low_surv_df = x_train.loc[low_survival_index]\n", |
|
|
1348 |
"print(f'Number of low survival probability patients: {len(low_survival_index)}')\n", |
|
|
1349 |
"print(low_survival_index)\n", |
|
|
1350 |
"\n", |
|
|
1351 |
"\"\"\" High survival probability patients \"\"\"\n", |
|
|
1352 |
"alive_df = y_train.loc[y_train['Event'] == 0]\n", |
|
|
1353 |
"high_survival_index = alive_df.loc[alive_df['SurvivalTime'] >= 1500].index\n", |
|
|
1354 |
"high_surv_df = x_train.loc[high_survival_index]\n", |
|
|
1355 |
"print(f'Number of high survival probability patients: {len(high_survival_index)}')\n", |
|
|
1356 |
"print(high_survival_index)" |
|
|
1357 |
], |
|
|
1358 |
"execution_count": 0, |
|
|
1359 |
"outputs": [ |
|
|
1360 |
{ |
|
|
1361 |
"output_type": "stream", |
|
|
1362 |
"text": [ |
|
|
1363 |
"Number of low survival probability patients: 101\n", |
|
|
1364 |
"Int64Index([371, 384, 100, 173, 83, 423, 149, 316, 102, 249,\n", |
|
|
1365 |
" ...\n", |
|
|
1366 |
" 112, 345, 299, 354, 373, 295, 213, 105, 56, 365],\n", |
|
|
1367 |
" dtype='int64', name='PatientID', length=101)\n", |
|
|
1368 |
"Number of high survival probability patients: 49\n", |
|
|
1369 |
"Int64Index([284, 394, 178, 304, 421, 143, 180, 53, 94, 338, 380, 48, 334,\n", |
|
|
1370 |
" 39, 257, 331, 403, 388, 151, 308, 130, 91, 275, 115, 42, 406,\n", |
|
|
1371 |
" 305, 25, 184, 95, 131, 256, 148, 297, 393, 84, 36, 163, 391,\n", |
|
|
1372 |
" 11, 141, 279, 154, 362, 381, 69, 58, 261, 273],\n", |
|
|
1373 |
" dtype='int64', name='PatientID')\n" |
|
|
1374 |
], |
|
|
1375 |
"name": "stdout" |
|
|
1376 |
} |
|
|
1377 |
] |
|
|
1378 |
}, |
|
|
1379 |
{ |
|
|
1380 |
"cell_type": "code", |
|
|
1381 |
"metadata": { |
|
|
1382 |
"id": "HdD4J_LhZZTk", |
|
|
1383 |
"colab_type": "code", |
|
|
1384 |
"colab": {} |
|
|
1385 |
}, |
|
|
1386 |
"source": [ |
|
|
1387 |
"\"\"\" Print features of the population having a small range Q3 - Q1 \"\"\"\n", |
|
|
1388 |
"def small_range_Q3_Q1(df, threshold, title):\n", |
|
|
1389 |
" descip = df.describe()\n", |
|
|
1390 |
" range_df = descip.loc['75%'] - descip.loc['25%']\n", |
|
|
1391 |
"\n", |
|
|
1392 |
" fig, axs = plt.subplots(1, 2, figsize=(18, 6))\n", |
|
|
1393 |
"\n", |
|
|
1394 |
" axs[0].hist(range_df, 100)\n", |
|
|
1395 |
" axs[0].set_xlabel('Value of the range Q3 - Q1')\n", |
|
|
1396 |
" axs[0].set_title('Histogram of all the features according to their Q3 - Q1 range')\n", |
|
|
1397 |
" small_range_id = range_df.loc[range_df < threshold]\n", |
|
|
1398 |
"\n", |
|
|
1399 |
" low_range_features = small_range_id.axes[0]\n", |
|
|
1400 |
" df[low_range_features].boxplot(ax=axs[1], figsize=(8,6), rot=90)\n", |
|
|
1401 |
" axs[1].set_title(f'Boxplot of the pop for only features with Q3 - Q1 < {threshold}')\n", |
|
|
1402 |
"\n", |
|
|
1403 |
" fig.suptitle(title)\n", |
|
|
1404 |
"\n", |
|
|
1405 |
" return small_range_id.axes[0]" |
|
|
1406 |
], |
|
|
1407 |
"execution_count": 0, |
|
|
1408 |
"outputs": [] |
|
|
1409 |
}, |
|
|
1410 |
{ |
|
|
1411 |
"cell_type": "code", |
|
|
1412 |
"metadata": { |
|
|
1413 |
"id": "SvvOIXy1UAJK", |
|
|
1414 |
"colab_type": "code", |
|
|
1415 |
"outputId": "4374616a-06ca-47d5-8e97-d0b2b584bc62", |
|
|
1416 |
"colab": { |
|
|
1417 |
"base_uri": "https://localhost:8080/", |
|
|
1418 |
"height": 1000 |
|
|
1419 |
} |
|
|
1420 |
}, |
|
|
1421 |
"source": [ |
|
|
1422 |
"\"\"\" Statistics of the low survival probability population \"\"\"\n", |
|
|
1423 |
"threshold = 0.1\n", |
|
|
1424 |
"low_surv_df.boxplot(figsize=(18,12), rot=90)\n", |
|
|
1425 |
"low_range_features_low_surv = small_range_Q3_Q1(low_surv_df, threshold, 'Low survival probability population')\n", |
|
|
1426 |
"low_surv_df.describe()" |
|
|
1427 |
], |
|
|
1428 |
"execution_count": 0, |
|
|
1429 |
"outputs": [ |
|
|
1430 |
{ |
|
|
1431 |
"output_type": "execute_result", |
|
|
1432 |
"data": { |
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1433 |
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1447 |
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1448 |
"<table border=\"1\" class=\"dataframe\">\n", |
|
|
1449 |
" <thead>\n", |
|
|
1450 |
" <tr style=\"text-align: right;\">\n", |
|
|
1451 |
" <th></th>\n", |
|
|
1452 |
" <th>original_shape_Compactness1</th>\n", |
|
|
1453 |
" <th>original_shape_Compactness2</th>\n", |
|
|
1454 |
" <th>original_shape_Maximum3DDiameter</th>\n", |
|
|
1455 |
" <th>original_shape_SphericalDisproportion</th>\n", |
|
|
1456 |
" <th>original_shape_Sphericity</th>\n", |
|
|
1457 |
" <th>original_shape_SurfaceArea</th>\n", |
|
|
1458 |
" <th>original_shape_SurfaceVolumeRatio</th>\n", |
|
|
1459 |
" <th>original_shape_VoxelVolume</th>\n", |
|
|
1460 |
" <th>original_firstorder_Energy</th>\n", |
|
|
1461 |
" <th>original_firstorder_Entropy</th>\n", |
|
|
1462 |
" <th>original_firstorder_Kurtosis</th>\n", |
|
|
1463 |
" <th>original_firstorder_Maximum</th>\n", |
|
|
1464 |
" <th>original_firstorder_Mean</th>\n", |
|
|
1465 |
" <th>original_firstorder_MeanAbsoluteDeviation</th>\n", |
|
|
1466 |
" <th>original_firstorder_Median</th>\n", |
|
|
1467 |
" <th>original_firstorder_Minimum</th>\n", |
|
|
1468 |
" <th>original_firstorder_Range</th>\n", |
|
|
1469 |
" <th>original_firstorder_RootMeanSquared</th>\n", |
|
|
1470 |
" <th>original_firstorder_Skewness</th>\n", |
|
|
1471 |
" <th>original_firstorder_StandardDeviation</th>\n", |
|
|
1472 |
" <th>original_firstorder_Uniformity</th>\n", |
|
|
1473 |
" <th>original_firstorder_Variance</th>\n", |
|
|
1474 |
" <th>original_glcm_Autocorrelation</th>\n", |
|
|
1475 |
" <th>original_glcm_ClusterProminence</th>\n", |
|
|
1476 |
" <th>original_glcm_ClusterShade</th>\n", |
|
|
1477 |
" <th>original_glcm_ClusterTendency</th>\n", |
|
|
1478 |
" <th>original_glcm_Contrast</th>\n", |
|
|
1479 |
" <th>original_glcm_Correlation</th>\n", |
|
|
1480 |
" <th>original_glcm_DifferenceEntropy</th>\n", |
|
|
1481 |
" <th>original_glcm_DifferenceAverage</th>\n", |
|
|
1482 |
" <th>original_glcm_JointEnergy</th>\n", |
|
|
1483 |
" <th>original_glcm_JointEntropy</th>\n", |
|
|
1484 |
" <th>original_glcm_Id</th>\n", |
|
|
1485 |
" <th>original_glcm_Idm</th>\n", |
|
|
1486 |
" <th>original_glcm_Imc1</th>\n", |
|
|
1487 |
" <th>original_glcm_Imc2</th>\n", |
|
|
1488 |
" <th>original_glcm_Idmn</th>\n", |
|
|
1489 |
" <th>original_glcm_Idn</th>\n", |
|
|
1490 |
" <th>original_glcm_InverseVariance</th>\n", |
|
|
1491 |
" <th>original_glcm_MaximumProbability</th>\n", |
|
|
1492 |
" <th>original_glcm_SumAverage</th>\n", |
|
|
1493 |
" <th>original_glcm_SumEntropy</th>\n", |
|
|
1494 |
" <th>original_glrlm_ShortRunEmphasis</th>\n", |
|
|
1495 |
" <th>original_glrlm_LongRunEmphasis</th>\n", |
|
|
1496 |
" <th>original_glrlm_GrayLevelNonUniformity</th>\n", |
|
|
1497 |
" <th>original_glrlm_RunLengthNonUniformity</th>\n", |
|
|
1498 |
" <th>original_glrlm_RunPercentage</th>\n", |
|
|
1499 |
" <th>original_glrlm_LowGrayLevelRunEmphasis</th>\n", |
|
|
1500 |
" <th>original_glrlm_HighGrayLevelRunEmphasis</th>\n", |
|
|
1501 |
" <th>original_glrlm_ShortRunLowGrayLevelEmphasis</th>\n", |
|
|
1502 |
" <th>original_glrlm_ShortRunHighGrayLevelEmphasis</th>\n", |
|
|
1503 |
" <th>original_glrlm_LongRunLowGrayLevelEmphasis</th>\n", |
|
|
1504 |
" <th>original_glrlm_LongRunHighGrayLevelEmphasis</th>\n", |
|
|
1505 |
" <th>Mstage</th>\n", |
|
|
1506 |
" <th>Nstage</th>\n", |
|
|
1507 |
" <th>SourceDataset</th>\n", |
|
|
1508 |
" <th>Tstage</th>\n", |
|
|
1509 |
" <th>age</th>\n", |
|
|
1510 |
" <th>Histology_adenocarcinoma</th>\n", |
|
|
1511 |
" <th>Histology_large cell</th>\n", |
|
|
1512 |
" <th>Histology_nos</th>\n", |
|
|
1513 |
" <th>Histology_squamous cell carcinoma</th>\n", |
|
|
1514 |
" </tr>\n", |
|
|
1515 |
" </thead>\n", |
|
|
1516 |
" <tbody>\n", |
|
|
1517 |
" <tr>\n", |
|
|
1518 |
" <th>count</th>\n", |
|
|
1519 |
" <td>101.000000</td>\n", |
|
|
1520 |
" <td>101.000000</td>\n", |
|
|
1521 |
" <td>101.000000</td>\n", |
|
|
1522 |
" <td>101.000000</td>\n", |
|
|
1523 |
" <td>101.000000</td>\n", |
|
|
1524 |
" <td>101.000000</td>\n", |
|
|
1525 |
" <td>101.000000</td>\n", |
|
|
1526 |
" <td>101.000000</td>\n", |
|
|
1527 |
" <td>101.000000</td>\n", |
|
|
1528 |
" <td>101.000000</td>\n", |
|
|
1529 |
" <td>101.000000</td>\n", |
|
|
1530 |
" <td>101.000000</td>\n", |
|
|
1531 |
" <td>101.000000</td>\n", |
|
|
1532 |
" <td>101.000000</td>\n", |
|
|
1533 |
" <td>101.000000</td>\n", |
|
|
1534 |
" <td>101.000000</td>\n", |
|
|
1535 |
" <td>101.000000</td>\n", |
|
|
1536 |
" <td>101.000000</td>\n", |
|
|
1537 |
" <td>101.000000</td>\n", |
|
|
1538 |
" <td>101.000000</td>\n", |
|
|
1539 |
" <td>101.000000</td>\n", |
|
|
1540 |
" <td>101.000000</td>\n", |
|
|
1541 |
" <td>101.000000</td>\n", |
|
|
1542 |
" <td>101.000000</td>\n", |
|
|
1543 |
" <td>101.000000</td>\n", |
|
|
1544 |
" <td>101.000000</td>\n", |
|
|
1545 |
" <td>101.000000</td>\n", |
|
|
1546 |
" <td>101.000000</td>\n", |
|
|
1547 |
" <td>101.000000</td>\n", |
|
|
1548 |
" <td>101.000000</td>\n", |
|
|
1549 |
" <td>101.000000</td>\n", |
|
|
1550 |
" <td>101.000000</td>\n", |
|
|
1551 |
" <td>101.000000</td>\n", |
|
|
1552 |
" <td>101.000000</td>\n", |
|
|
1553 |
" <td>101.000000</td>\n", |
|
|
1554 |
" <td>101.000000</td>\n", |
|
|
1555 |
" <td>101.000000</td>\n", |
|
|
1556 |
" <td>101.000000</td>\n", |
|
|
1557 |
" <td>101.000000</td>\n", |
|
|
1558 |
" <td>101.000000</td>\n", |
|
|
1559 |
" <td>101.000000</td>\n", |
|
|
1560 |
" <td>101.000000</td>\n", |
|
|
1561 |
" <td>101.000000</td>\n", |
|
|
1562 |
" <td>101.000000</td>\n", |
|
|
1563 |
" <td>101.000000</td>\n", |
|
|
1564 |
" <td>101.000000</td>\n", |
|
|
1565 |
" <td>101.000000</td>\n", |
|
|
1566 |
" <td>101.000000</td>\n", |
|
|
1567 |
" <td>101.000000</td>\n", |
|
|
1568 |
" <td>101.000000</td>\n", |
|
|
1569 |
" <td>101.000000</td>\n", |
|
|
1570 |
" <td>101.000000</td>\n", |
|
|
1571 |
" <td>101.000000</td>\n", |
|
|
1572 |
" <td>101.000000</td>\n", |
|
|
1573 |
" <td>101.000000</td>\n", |
|
|
1574 |
" <td>101.000000</td>\n", |
|
|
1575 |
" <td>101.000000</td>\n", |
|
|
1576 |
" <td>101.000000</td>\n", |
|
|
1577 |
" <td>101.000000</td>\n", |
|
|
1578 |
" <td>101.000000</td>\n", |
|
|
1579 |
" <td>101.000000</td>\n", |
|
|
1580 |
" <td>101.000000</td>\n", |
|
|
1581 |
" </tr>\n", |
|
|
1582 |
" <tr>\n", |
|
|
1583 |
" <th>mean</th>\n", |
|
|
1584 |
" <td>0.476402</td>\n", |
|
|
1585 |
" <td>0.335141</td>\n", |
|
|
1586 |
" <td>0.321982</td>\n", |
|
|
1587 |
" <td>0.266604</td>\n", |
|
|
1588 |
" <td>0.529001</td>\n", |
|
|
1589 |
" <td>0.210168</td>\n", |
|
|
1590 |
" <td>0.272455</td>\n", |
|
|
1591 |
" <td>0.131952</td>\n", |
|
|
1592 |
" <td>0.061725</td>\n", |
|
|
1593 |
" <td>0.510419</td>\n", |
|
|
1594 |
" <td>0.118243</td>\n", |
|
|
1595 |
" <td>0.184491</td>\n", |
|
|
1596 |
" <td>0.775428</td>\n", |
|
|
1597 |
" <td>0.345475</td>\n", |
|
|
1598 |
" <td>0.858435</td>\n", |
|
|
1599 |
" <td>0.307685</td>\n", |
|
|
1600 |
" <td>0.249277</td>\n", |
|
|
1601 |
" <td>0.275224</td>\n", |
|
|
1602 |
" <td>0.611627</td>\n", |
|
|
1603 |
" <td>0.408463</td>\n", |
|
|
1604 |
" <td>0.294907</td>\n", |
|
|
1605 |
" <td>0.242956</td>\n", |
|
|
1606 |
" <td>0.605381</td>\n", |
|
|
1607 |
" <td>0.118148</td>\n", |
|
|
1608 |
" <td>0.387256</td>\n", |
|
|
1609 |
" <td>0.219607</td>\n", |
|
|
1610 |
" <td>0.084823</td>\n", |
|
|
1611 |
" <td>0.775846</td>\n", |
|
|
1612 |
" <td>0.396232</td>\n", |
|
|
1613 |
" <td>0.187427</td>\n", |
|
|
1614 |
" <td>0.166168</td>\n", |
|
|
1615 |
" <td>0.438821</td>\n", |
|
|
1616 |
" <td>0.581820</td>\n", |
|
|
1617 |
" <td>0.566204</td>\n", |
|
|
1618 |
" <td>0.485035</td>\n", |
|
|
1619 |
" <td>0.787726</td>\n", |
|
|
1620 |
" <td>0.870571</td>\n", |
|
|
1621 |
" <td>0.754060</td>\n", |
|
|
1622 |
" <td>0.598113</td>\n", |
|
|
1623 |
" <td>0.241447</td>\n", |
|
|
1624 |
" <td>0.686037</td>\n", |
|
|
1625 |
" <td>0.500815</td>\n", |
|
|
1626 |
" <td>0.589580</td>\n", |
|
|
1627 |
" <td>0.146258</td>\n", |
|
|
1628 |
" <td>0.105473</td>\n", |
|
|
1629 |
" <td>0.110472</td>\n", |
|
|
1630 |
" <td>0.530723</td>\n", |
|
|
1631 |
" <td>0.039752</td>\n", |
|
|
1632 |
" <td>0.569912</td>\n", |
|
|
1633 |
" <td>0.047246</td>\n", |
|
|
1634 |
" <td>0.509542</td>\n", |
|
|
1635 |
" <td>0.035881</td>\n", |
|
|
1636 |
" <td>0.205714</td>\n", |
|
|
1637 |
" <td>0.029703</td>\n", |
|
|
1638 |
" <td>0.321782</td>\n", |
|
|
1639 |
" <td>0.158416</td>\n", |
|
|
1640 |
" <td>0.344059</td>\n", |
|
|
1641 |
" <td>0.558008</td>\n", |
|
|
1642 |
" <td>0.227723</td>\n", |
|
|
1643 |
" <td>0.217822</td>\n", |
|
|
1644 |
" <td>0.178218</td>\n", |
|
|
1645 |
" <td>0.316832</td>\n", |
|
|
1646 |
" </tr>\n", |
|
|
1647 |
" <tr>\n", |
|
|
1648 |
" <th>std</th>\n", |
|
|
1649 |
" <td>0.173457</td>\n", |
|
|
1650 |
" <td>0.166450</td>\n", |
|
|
1651 |
" <td>0.195652</td>\n", |
|
|
1652 |
" <td>0.153115</td>\n", |
|
|
1653 |
" <td>0.172619</td>\n", |
|
|
1654 |
" <td>0.183855</td>\n", |
|
|
1655 |
" <td>0.211392</td>\n", |
|
|
1656 |
" <td>0.155133</td>\n", |
|
|
1657 |
" <td>0.085546</td>\n", |
|
|
1658 |
" <td>0.242460</td>\n", |
|
|
1659 |
" <td>0.181936</td>\n", |
|
|
1660 |
" <td>0.165187</td>\n", |
|
|
1661 |
" <td>0.172660</td>\n", |
|
|
1662 |
" <td>0.238864</td>\n", |
|
|
1663 |
" <td>0.161358</td>\n", |
|
|
1664 |
" <td>0.147532</td>\n", |
|
|
1665 |
" <td>0.156409</td>\n", |
|
|
1666 |
" <td>0.186487</td>\n", |
|
|
1667 |
" <td>0.170562</td>\n", |
|
|
1668 |
" <td>0.218355</td>\n", |
|
|
1669 |
" <td>0.212134</td>\n", |
|
|
1670 |
" <td>0.203402</td>\n", |
|
|
1671 |
" <td>0.219387</td>\n", |
|
|
1672 |
" <td>0.139078</td>\n", |
|
|
1673 |
" <td>0.100801</td>\n", |
|
|
1674 |
" <td>0.192068</td>\n", |
|
|
1675 |
" <td>0.111087</td>\n", |
|
|
1676 |
" <td>0.153960</td>\n", |
|
|
1677 |
" <td>0.204374</td>\n", |
|
|
1678 |
" <td>0.158845</td>\n", |
|
|
1679 |
" <td>0.151797</td>\n", |
|
|
1680 |
" <td>0.229303</td>\n", |
|
|
1681 |
" <td>0.236810</td>\n", |
|
|
1682 |
" <td>0.245050</td>\n", |
|
|
1683 |
" <td>0.158270</td>\n", |
|
|
1684 |
" <td>0.154150</td>\n", |
|
|
1685 |
" <td>0.166189</td>\n", |
|
|
1686 |
" <td>0.200761</td>\n", |
|
|
1687 |
" <td>0.207235</td>\n", |
|
|
1688 |
" <td>0.166099</td>\n", |
|
|
1689 |
" <td>0.197625</td>\n", |
|
|
1690 |
" <td>0.242696</td>\n", |
|
|
1691 |
" <td>0.225605</td>\n", |
|
|
1692 |
" <td>0.141577</td>\n", |
|
|
1693 |
" <td>0.156377</td>\n", |
|
|
1694 |
" <td>0.114490</td>\n", |
|
|
1695 |
" <td>0.245564</td>\n", |
|
|
1696 |
" <td>0.106124</td>\n", |
|
|
1697 |
" <td>0.215871</td>\n", |
|
|
1698 |
" <td>0.107909</td>\n", |
|
|
1699 |
" <td>0.181075</td>\n", |
|
|
1700 |
" <td>0.113548</td>\n", |
|
|
1701 |
" <td>0.162696</td>\n", |
|
|
1702 |
" <td>0.170613</td>\n", |
|
|
1703 |
" <td>0.300701</td>\n", |
|
|
1704 |
" <td>0.366952</td>\n", |
|
|
1705 |
" <td>0.257030</td>\n", |
|
|
1706 |
" <td>0.196801</td>\n", |
|
|
1707 |
" <td>0.421454</td>\n", |
|
|
1708 |
" <td>0.414824</td>\n", |
|
|
1709 |
" <td>0.384605</td>\n", |
|
|
1710 |
" <td>0.467562</td>\n", |
|
|
1711 |
" </tr>\n", |
|
|
1712 |
" <tr>\n", |
|
|
1713 |
" <th>min</th>\n", |
|
|
1714 |
" <td>0.045788</td>\n", |
|
|
1715 |
" <td>0.017690</td>\n", |
|
|
1716 |
" <td>0.023903</td>\n", |
|
|
1717 |
" <td>0.057762</td>\n", |
|
|
1718 |
" <td>0.060644</td>\n", |
|
|
1719 |
" <td>0.003058</td>\n", |
|
|
1720 |
" <td>0.026136</td>\n", |
|
|
1721 |
" <td>0.000474</td>\n", |
|
|
1722 |
" <td>0.000357</td>\n", |
|
|
1723 |
" <td>0.071819</td>\n", |
|
|
1724 |
" <td>0.003333</td>\n", |
|
|
1725 |
" <td>0.029309</td>\n", |
|
|
1726 |
" <td>0.279380</td>\n", |
|
|
1727 |
" <td>0.016698</td>\n", |
|
|
1728 |
" <td>0.108265</td>\n", |
|
|
1729 |
" <td>0.174419</td>\n", |
|
|
1730 |
" <td>0.028302</td>\n", |
|
|
1731 |
" <td>0.028038</td>\n", |
|
|
1732 |
" <td>0.000000</td>\n", |
|
|
1733 |
" <td>0.042157</td>\n", |
|
|
1734 |
" <td>0.010855</td>\n", |
|
|
1735 |
" <td>0.007781</td>\n", |
|
|
1736 |
" <td>0.131618</td>\n", |
|
|
1737 |
" <td>0.000688</td>\n", |
|
|
1738 |
" <td>0.094170</td>\n", |
|
|
1739 |
" <td>0.006610</td>\n", |
|
|
1740 |
" <td>0.000359</td>\n", |
|
|
1741 |
" <td>0.203515</td>\n", |
|
|
1742 |
" <td>0.000000</td>\n", |
|
|
1743 |
" <td>0.000000</td>\n", |
|
|
1744 |
" <td>0.003165</td>\n", |
|
|
1745 |
" <td>0.016297</td>\n", |
|
|
1746 |
" <td>0.040142</td>\n", |
|
|
1747 |
" <td>0.032800</td>\n", |
|
|
1748 |
" <td>0.000000</td>\n", |
|
|
1749 |
" <td>0.000000</td>\n", |
|
|
1750 |
" <td>0.179424</td>\n", |
|
|
1751 |
" <td>0.142017</td>\n", |
|
|
1752 |
" <td>0.055881</td>\n", |
|
|
1753 |
" <td>0.004558</td>\n", |
|
|
1754 |
" <td>0.053378</td>\n", |
|
|
1755 |
" <td>0.000000</td>\n", |
|
|
1756 |
" <td>0.000000</td>\n", |
|
|
1757 |
" <td>0.002510</td>\n", |
|
|
1758 |
" <td>0.000213</td>\n", |
|
|
1759 |
" <td>0.000952</td>\n", |
|
|
1760 |
" <td>0.000000</td>\n", |
|
|
1761 |
" <td>0.000492</td>\n", |
|
|
1762 |
" <td>0.132005</td>\n", |
|
|
1763 |
" <td>0.000420</td>\n", |
|
|
1764 |
" <td>0.106711</td>\n", |
|
|
1765 |
" <td>0.003397</td>\n", |
|
|
1766 |
" <td>0.011810</td>\n", |
|
|
1767 |
" <td>0.000000</td>\n", |
|
|
1768 |
" <td>0.000000</td>\n", |
|
|
1769 |
" <td>0.000000</td>\n", |
|
|
1770 |
" <td>0.000000</td>\n", |
|
|
1771 |
" <td>0.015641</td>\n", |
|
|
1772 |
" <td>0.000000</td>\n", |
|
|
1773 |
" <td>0.000000</td>\n", |
|
|
1774 |
" <td>0.000000</td>\n", |
|
|
1775 |
" <td>0.000000</td>\n", |
|
|
1776 |
" </tr>\n", |
|
|
1777 |
" <tr>\n", |
|
|
1778 |
" <th>25%</th>\n", |
|
|
1779 |
" <td>0.374122</td>\n", |
|
|
1780 |
" <td>0.223535</td>\n", |
|
|
1781 |
" <td>0.162474</td>\n", |
|
|
1782 |
" <td>0.169819</td>\n", |
|
|
1783 |
" <td>0.433796</td>\n", |
|
|
1784 |
" <td>0.051512</td>\n", |
|
|
1785 |
" <td>0.128843</td>\n", |
|
|
1786 |
" <td>0.016345</td>\n", |
|
|
1787 |
" <td>0.012152</td>\n", |
|
|
1788 |
" <td>0.317655</td>\n", |
|
|
1789 |
" <td>0.018154</td>\n", |
|
|
1790 |
" <td>0.073272</td>\n", |
|
|
1791 |
" <td>0.671351</td>\n", |
|
|
1792 |
" <td>0.147928</td>\n", |
|
|
1793 |
" <td>0.862631</td>\n", |
|
|
1794 |
" <td>0.186047</td>\n", |
|
|
1795 |
" <td>0.142367</td>\n", |
|
|
1796 |
" <td>0.119388</td>\n", |
|
|
1797 |
" <td>0.527886</td>\n", |
|
|
1798 |
" <td>0.220675</td>\n", |
|
|
1799 |
" <td>0.105008</td>\n", |
|
|
1800 |
" <td>0.074269</td>\n", |
|
|
1801 |
" <td>0.452139</td>\n", |
|
|
1802 |
" <td>0.030397</td>\n", |
|
|
1803 |
" <td>0.341826</td>\n", |
|
|
1804 |
" <td>0.063276</td>\n", |
|
|
1805 |
" <td>0.022835</td>\n", |
|
|
1806 |
" <td>0.694403</td>\n", |
|
|
1807 |
" <td>0.247110</td>\n", |
|
|
1808 |
" <td>0.072918</td>\n", |
|
|
1809 |
" <td>0.047916</td>\n", |
|
|
1810 |
" <td>0.244542</td>\n", |
|
|
1811 |
" <td>0.394454</td>\n", |
|
|
1812 |
" <td>0.368093</td>\n", |
|
|
1813 |
" <td>0.380593</td>\n", |
|
|
1814 |
" <td>0.752825</td>\n", |
|
|
1815 |
" <td>0.838722</td>\n", |
|
|
1816 |
" <td>0.668426</td>\n", |
|
|
1817 |
" <td>0.482752</td>\n", |
|
|
1818 |
" <td>0.118431</td>\n", |
|
|
1819 |
" <td>0.553587</td>\n", |
|
|
1820 |
" <td>0.302171</td>\n", |
|
|
1821 |
" <td>0.423152</td>\n", |
|
|
1822 |
" <td>0.047774</td>\n", |
|
|
1823 |
" <td>0.007983</td>\n", |
|
|
1824 |
" <td>0.023171</td>\n", |
|
|
1825 |
" <td>0.338877</td>\n", |
|
|
1826 |
" <td>0.007635</td>\n", |
|
|
1827 |
" <td>0.399738</td>\n", |
|
|
1828 |
" <td>0.010350</td>\n", |
|
|
1829 |
" <td>0.373402</td>\n", |
|
|
1830 |
" <td>0.008025</td>\n", |
|
|
1831 |
" <td>0.084520</td>\n", |
|
|
1832 |
" <td>0.000000</td>\n", |
|
|
1833 |
" <td>0.000000</td>\n", |
|
|
1834 |
" <td>0.000000</td>\n", |
|
|
1835 |
" <td>0.250000</td>\n", |
|
|
1836 |
" <td>0.440696</td>\n", |
|
|
1837 |
" <td>0.000000</td>\n", |
|
|
1838 |
" <td>0.000000</td>\n", |
|
|
1839 |
" <td>0.000000</td>\n", |
|
|
1840 |
" <td>0.000000</td>\n", |
|
|
1841 |
" </tr>\n", |
|
|
1842 |
" <tr>\n", |
|
|
1843 |
" <th>50%</th>\n", |
|
|
1844 |
" <td>0.475980</td>\n", |
|
|
1845 |
" <td>0.315574</td>\n", |
|
|
1846 |
" <td>0.291289</td>\n", |
|
|
1847 |
" <td>0.238690</td>\n", |
|
|
1848 |
" <td>0.535424</td>\n", |
|
|
1849 |
" <td>0.168099</td>\n", |
|
|
1850 |
" <td>0.205094</td>\n", |
|
|
1851 |
" <td>0.078499</td>\n", |
|
|
1852 |
" <td>0.031150</td>\n", |
|
|
1853 |
" <td>0.482425</td>\n", |
|
|
1854 |
" <td>0.061671</td>\n", |
|
|
1855 |
" <td>0.125199</td>\n", |
|
|
1856 |
" <td>0.835057</td>\n", |
|
|
1857 |
" <td>0.297312</td>\n", |
|
|
1858 |
" <td>0.923166</td>\n", |
|
|
1859 |
" <td>0.250646</td>\n", |
|
|
1860 |
" <td>0.205832</td>\n", |
|
|
1861 |
" <td>0.236759</td>\n", |
|
|
1862 |
" <td>0.612533</td>\n", |
|
|
1863 |
" <td>0.381681</td>\n", |
|
|
1864 |
" <td>0.264920</td>\n", |
|
|
1865 |
" <td>0.180771</td>\n", |
|
|
1866 |
" <td>0.643047</td>\n", |
|
|
1867 |
" <td>0.075532</td>\n", |
|
|
1868 |
" <td>0.404867</td>\n", |
|
|
1869 |
" <td>0.156279</td>\n", |
|
|
1870 |
" <td>0.047242</td>\n", |
|
|
1871 |
" <td>0.801636</td>\n", |
|
|
1872 |
" <td>0.364809</td>\n", |
|
|
1873 |
" <td>0.132452</td>\n", |
|
|
1874 |
" <td>0.127012</td>\n", |
|
|
1875 |
" <td>0.425570</td>\n", |
|
|
1876 |
" <td>0.643137</td>\n", |
|
|
1877 |
" <td>0.626659</td>\n", |
|
|
1878 |
" <td>0.475598</td>\n", |
|
|
1879 |
" <td>0.818568</td>\n", |
|
|
1880 |
" <td>0.932224</td>\n", |
|
|
1881 |
" <td>0.821191</td>\n", |
|
|
1882 |
" <td>0.646767</td>\n", |
|
|
1883 |
" <td>0.214629</td>\n", |
|
|
1884 |
" <td>0.723143</td>\n", |
|
|
1885 |
" <td>0.484535</td>\n", |
|
|
1886 |
" <td>0.569787</td>\n", |
|
|
1887 |
" <td>0.121663</td>\n", |
|
|
1888 |
" <td>0.042527</td>\n", |
|
|
1889 |
" <td>0.077018</td>\n", |
|
|
1890 |
" <td>0.500447</td>\n", |
|
|
1891 |
" <td>0.013177</td>\n", |
|
|
1892 |
" <td>0.584989</td>\n", |
|
|
1893 |
" <td>0.018849</td>\n", |
|
|
1894 |
" <td>0.540478</td>\n", |
|
|
1895 |
" <td>0.013592</td>\n", |
|
|
1896 |
" <td>0.167268</td>\n", |
|
|
1897 |
" <td>0.000000</td>\n", |
|
|
1898 |
" <td>0.500000</td>\n", |
|
|
1899 |
" <td>0.000000</td>\n", |
|
|
1900 |
" <td>0.250000</td>\n", |
|
|
1901 |
" <td>0.579104</td>\n", |
|
|
1902 |
" <td>0.000000</td>\n", |
|
|
1903 |
" <td>0.000000</td>\n", |
|
|
1904 |
" <td>0.000000</td>\n", |
|
|
1905 |
" <td>0.000000</td>\n", |
|
|
1906 |
" </tr>\n", |
|
|
1907 |
" <tr>\n", |
|
|
1908 |
" <th>75%</th>\n", |
|
|
1909 |
" <td>0.584234</td>\n", |
|
|
1910 |
" <td>0.428020</td>\n", |
|
|
1911 |
" <td>0.464677</td>\n", |
|
|
1912 |
" <td>0.320481</td>\n", |
|
|
1913 |
" <td>0.638526</td>\n", |
|
|
1914 |
" <td>0.314406</td>\n", |
|
|
1915 |
" <td>0.363445</td>\n", |
|
|
1916 |
" <td>0.171272</td>\n", |
|
|
1917 |
" <td>0.066572</td>\n", |
|
|
1918 |
" <td>0.731944</td>\n", |
|
|
1919 |
" <td>0.144419</td>\n", |
|
|
1920 |
" <td>0.238930</td>\n", |
|
|
1921 |
" <td>0.905610</td>\n", |
|
|
1922 |
" <td>0.518386</td>\n", |
|
|
1923 |
" <td>0.940629</td>\n", |
|
|
1924 |
" <td>0.399225</td>\n", |
|
|
1925 |
" <td>0.312464</td>\n", |
|
|
1926 |
" <td>0.417599</td>\n", |
|
|
1927 |
" <td>0.721518</td>\n", |
|
|
1928 |
" <td>0.563368</td>\n", |
|
|
1929 |
" <td>0.436437</td>\n", |
|
|
1930 |
" <td>0.353959</td>\n", |
|
|
1931 |
" <td>0.787601</td>\n", |
|
|
1932 |
" <td>0.166475</td>\n", |
|
|
1933 |
" <td>0.437492</td>\n", |
|
|
1934 |
" <td>0.310728</td>\n", |
|
|
1935 |
" <td>0.106296</td>\n", |
|
|
1936 |
" <td>0.871950</td>\n", |
|
|
1937 |
" <td>0.524123</td>\n", |
|
|
1938 |
" <td>0.243542</td>\n", |
|
|
1939 |
" <td>0.246711</td>\n", |
|
|
1940 |
" <td>0.621432</td>\n", |
|
|
1941 |
" <td>0.771233</td>\n", |
|
|
1942 |
" <td>0.766459</td>\n", |
|
|
1943 |
" <td>0.584677</td>\n", |
|
|
1944 |
" <td>0.873934</td>\n", |
|
|
1945 |
" <td>0.974168</td>\n", |
|
|
1946 |
" <td>0.893074</td>\n", |
|
|
1947 |
" <td>0.738960</td>\n", |
|
|
1948 |
" <td>0.337898</td>\n", |
|
|
1949 |
" <td>0.848430</td>\n", |
|
|
1950 |
" <td>0.708227</td>\n", |
|
|
1951 |
" <td>0.779072</td>\n", |
|
|
1952 |
" <td>0.201804</td>\n", |
|
|
1953 |
" <td>0.121800</td>\n", |
|
|
1954 |
" <td>0.160963</td>\n", |
|
|
1955 |
" <td>0.723372</td>\n", |
|
|
1956 |
" <td>0.038828</td>\n", |
|
|
1957 |
" <td>0.736627</td>\n", |
|
|
1958 |
" <td>0.046370</td>\n", |
|
|
1959 |
" <td>0.640683</td>\n", |
|
|
1960 |
" <td>0.020318</td>\n", |
|
|
1961 |
" <td>0.280116</td>\n", |
|
|
1962 |
" <td>0.000000</td>\n", |
|
|
1963 |
" <td>0.500000</td>\n", |
|
|
1964 |
" <td>0.000000</td>\n", |
|
|
1965 |
" <td>0.500000</td>\n", |
|
|
1966 |
" <td>0.688819</td>\n", |
|
|
1967 |
" <td>0.000000</td>\n", |
|
|
1968 |
" <td>0.000000</td>\n", |
|
|
1969 |
" <td>0.000000</td>\n", |
|
|
1970 |
" <td>1.000000</td>\n", |
|
|
1971 |
" </tr>\n", |
|
|
1972 |
" <tr>\n", |
|
|
1973 |
" <th>max</th>\n", |
|
|
1974 |
" <td>0.826667</td>\n", |
|
|
1975 |
" <td>0.734517</td>\n", |
|
|
1976 |
" <td>1.000000</td>\n", |
|
|
1977 |
" <td>0.848415</td>\n", |
|
|
1978 |
" <td>0.854953</td>\n", |
|
|
1979 |
" <td>0.706103</td>\n", |
|
|
1980 |
" <td>0.985167</td>\n", |
|
|
1981 |
" <td>0.757682</td>\n", |
|
|
1982 |
" <td>0.388404</td>\n", |
|
|
1983 |
" <td>0.937622</td>\n", |
|
|
1984 |
" <td>0.959661</td>\n", |
|
|
1985 |
" <td>1.000000</td>\n", |
|
|
1986 |
" <td>1.000000</td>\n", |
|
|
1987 |
" <td>0.955959</td>\n", |
|
|
1988 |
" <td>0.990687</td>\n", |
|
|
1989 |
" <td>0.863049</td>\n", |
|
|
1990 |
" <td>1.000000</td>\n", |
|
|
1991 |
" <td>0.845946</td>\n", |
|
|
1992 |
" <td>1.000000</td>\n", |
|
|
1993 |
" <td>0.990265</td>\n", |
|
|
1994 |
" <td>1.000000</td>\n", |
|
|
1995 |
" <td>0.982058</td>\n", |
|
|
1996 |
" <td>1.000000</td>\n", |
|
|
1997 |
" <td>1.000000</td>\n", |
|
|
1998 |
" <td>1.000000</td>\n", |
|
|
1999 |
" <td>0.946078</td>\n", |
|
|
2000 |
" <td>0.675703</td>\n", |
|
|
2001 |
" <td>1.000000</td>\n", |
|
|
2002 |
" <td>0.935675</td>\n", |
|
|
2003 |
" <td>0.820194</td>\n", |
|
|
2004 |
" <td>1.000000</td>\n", |
|
|
2005 |
" <td>0.891901</td>\n", |
|
|
2006 |
" <td>1.000000</td>\n", |
|
|
2007 |
" <td>1.000000</td>\n", |
|
|
2008 |
" <td>1.000000</td>\n", |
|
|
2009 |
" <td>1.000000</td>\n", |
|
|
2010 |
" <td>1.000000</td>\n", |
|
|
2011 |
" <td>1.000000</td>\n", |
|
|
2012 |
" <td>0.971813</td>\n", |
|
|
2013 |
" <td>1.000000</td>\n", |
|
|
2014 |
" <td>1.000000</td>\n", |
|
|
2015 |
" <td>0.897109</td>\n", |
|
|
2016 |
" <td>0.983827</td>\n", |
|
|
2017 |
" <td>1.000000</td>\n", |
|
|
2018 |
" <td>0.979532</td>\n", |
|
|
2019 |
" <td>0.557070</td>\n", |
|
|
2020 |
" <td>0.979884</td>\n", |
|
|
2021 |
" <td>1.000000</td>\n", |
|
|
2022 |
" <td>1.000000</td>\n", |
|
|
2023 |
" <td>1.000000</td>\n", |
|
|
2024 |
" <td>1.000000</td>\n", |
|
|
2025 |
" <td>1.000000</td>\n", |
|
|
2026 |
" <td>0.763831</td>\n", |
|
|
2027 |
" <td>1.000000</td>\n", |
|
|
2028 |
" <td>0.750000</td>\n", |
|
|
2029 |
" <td>1.000000</td>\n", |
|
|
2030 |
" <td>0.750000</td>\n", |
|
|
2031 |
" <td>0.904993</td>\n", |
|
|
2032 |
" <td>1.000000</td>\n", |
|
|
2033 |
" <td>1.000000</td>\n", |
|
|
2034 |
" <td>1.000000</td>\n", |
|
|
2035 |
" <td>1.000000</td>\n", |
|
|
2036 |
" </tr>\n", |
|
|
2037 |
" </tbody>\n", |
|
|
2038 |
"</table>\n", |
|
|
2039 |
"</div>" |
|
|
2040 |
], |
|
|
2041 |
"text/plain": [ |
|
|
2042 |
" original_shape_Compactness1 ... Histology_squamous cell carcinoma\n", |
|
|
2043 |
"count 101.000000 ... 101.000000\n", |
|
|
2044 |
"mean 0.476402 ... 0.316832\n", |
|
|
2045 |
"std 0.173457 ... 0.467562\n", |
|
|
2046 |
"min 0.045788 ... 0.000000\n", |
|
|
2047 |
"25% 0.374122 ... 0.000000\n", |
|
|
2048 |
"50% 0.475980 ... 0.000000\n", |
|
|
2049 |
"75% 0.584234 ... 1.000000\n", |
|
|
2050 |
"max 0.826667 ... 1.000000\n", |
|
|
2051 |
"\n", |
|
|
2052 |
"[8 rows x 62 columns]" |
|
|
2053 |
] |
|
|
2054 |
}, |
|
|
2055 |
"metadata": { |
|
|
2056 |
"tags": [] |
|
|
2057 |
}, |
|
|
2058 |
"execution_count": 22 |
|
|
2059 |
}, |
|
|
2060 |
{ |
|
|
2061 |
"output_type": "display_data", |
|
|
2062 |
"data": { |
|
|
2063 |
"image/png": 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YXnhu8HZJmv5sXLOEma/Kw3VddTod+b7fX9bpdCp1nIF6IIlQsKrPb89YK0jS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\nMCszm/x+Y/jq7hnlBkkc3b9Wp2xd27xxa+PPJYlJSwF0jjT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4YMDkkEJQxBBHlBBUniEERBUVABCUNQwAAGGBRFggFEQTARBEEkZxDJkgRmSEMacpRoIomvwohk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\nEeke6FGSHlWVIjeVUw7xl6TA9g8BJD0Y+H9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|
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2064 |
"text/plain": [ |
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2065 |
"<Figure size 1296x864 with 1 Axes>" |
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2066 |
] |
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|
2067 |
}, |
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|
2068 |
"metadata": { |
|
|
2069 |
"tags": [] |
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|
2070 |
} |
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|
2071 |
}, |
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2072 |
{ |
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|
2073 |
"output_type": "display_data", |
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|
2074 |
"data": { |
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2075 |
"image/png": 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fK44JLwQeICcXho4/dwFHFt8vBi4gn8OeAG4BdiuZ7vCxe5R5tJKP4euK4+FjVZZhZ+Ba\n8rnyLuCYontn2finl41XcfpF7P8GXF/E/n3gT0vGq3purhDbAPBZ4Afk64WvU3IdArydfB59rBi2\ntWzbfKQ4Rv4B+E9Kzoclw72FfB55tliOi2uc9ojjcoXpVj1fFtP7FPkc9gRwE7B90W9OsX1bgHcC\nt5dN9yTg6xXmdzHwDPmGdmWxXKNexwFfId/QPU4+L+xRdD+2bFrfKN/vSuY54nqkWC+/Jl8HjHZe\n34J8Hf5osY5/COxYYbneOzT/4vsvgK+UfL8feHVpfKPEfw+jXCOVzfdT5HP3ZmXdFwG/AqLCOK8l\n/yZeu4HHk07yfcKWZd0PI+//W5V1/zTF/lrnfF4P/Dv5d/GGKsNcAXym5PsC4Nc1TPsBvG4Yexs0\nOoCGLnz9CYLvAu8uPm8FvL74PIeyG1/gaPKJ7KXFsF8FvlT0m1scENqAmeQL5WcYmSB4BjikOHg9\nD3hN8YNpKeY3CHygZH6JfGLahpxJXA0sKea/LfkE9J4q66FqrCXTXu8mraT/EcCLithOJh94tyhZ\nljETBJW2R8nwFxXr4FXFcrUW/U8EvgfsQk5m/DvQV2XaI+YNbEk+aL+3iHtP8g3h3KL/fOCVxfr/\nC3JC6JBRtvfwclaZ3wD5pnOPYn7bjjH/hykSLcALKG7eKyzXy8jFK2cBO5BPoGcX/WaQb6D+pVje\nLYC2ot87yYmH/0e+cXwZOQO7ebEvfJS8b/41+eLgz4rxLiaftPYu1s025Au+DxbjHkred0dLEPyA\nfMH3QvJ+fFzRb3/yvrMH8Hzyibnqvles078v+f5C8snk3cU67Si+v2ii97catvfmZfPeH1hL5Yu2\nS4DLS763Fes8kZMy+27g8e7Pi2nsXqHf6cB3yrrVkyC4g5yMel7J/rVzsY8cVsS9U8kx9hlygmoG\ncDw5IRQlx9nPk/e/NvJFx9Ax5CXki7UDi2nvU3zfYUPWiX/+Tfc/Rl7DPL84vlxa0v9S8rXD1sVx\n7P+AzqLfvuRj8p8Ux8WrS8a7mHxueHNxPDyHkddOpQmC0eZxVOl4VZbh28AXyeewV5MTDX9dy/iV\n+hexP0q+WWohJ9avLPqNem1QYfoD5HPpvGLc/yo5Xg09nNiHfH48hXx+nVmybZaTj50vJN+Mf7rK\nfOYz8lxay7RHHJfLpjfq+bJYrl8W83le8f2Mot8cnksQzCInUkqTEz8G3lFlOS4uXUbGuI4jX59u\nXfQ7G7ij2rTK97vyYYp1uJZccm9WsVyjndffB3yD/LuZQb4O36bCMr2UnEDYjHzeu3doWxX9/kBx\nE8/I30Wl+O+hyjVShfl+j7KkWNF992I+Ly/pdh05MZDIJQQ3qzTNGo4nVwKXVOjeUqzbfcq615wg\nIJdwOqVY5l8Cp1HheqVk+J8Ah5V8375YvorXfCXDmSCoZXs0OoCGLnz+Ia4sfthDf09SPUHwbfKF\n9PZl05nD+jeMS4D3l3z/M/JFcQvwcUYeAJ9PziKWJgi+PUbsHwC+VvI9kYsiD32/HVhU8v0LFDeO\nFaZVNdaSaVdNEFSY3h+AV5Usy8YmCHYp6fYD4F3F50FgQUm/nUrjHm0bkW9a/qdsmH+n7ClqSb+z\ngX8ZZXsPL2eV+Q0AnyzpP+r8yTeX76PCyWiMdX8I8OPi8xvIF1KV1seNwIkVur+JfEG4WUm3PooS\nFOQTWunF5Zspubkrut3K6AmCI0q+fw64oPi8GPhsSb+XjbbvsX6C4N3AD8qG+S5w1ETvb2Nt7wrz\nPoIqmW7gDOCmCt1fUuxnr6hnnygZv62IsdITquMoeapYdKsnQXD0GMPcARxcfD4KuKuk3/OL+byY\nXIx2LfD8kv6X8dwxZBElycuSffk9G7JO/PNvuv8x8jrnmeJ4/cqi3wzytcfckuHfBwyUfD8X+F/y\nTfCLSrpfTHFTXXzfivykftfieyqO4aPOg7Fv8Hctprt1SbfP8txT9LHGX69/Eft/lHw/EPhZ8bne\na4MBihvn4vvcYnlnkEuHfrmk32bFepxfsm2OK4ujWknN+Yw8l9Yy7arHZcY4XxbL9bGSfu8HvlV8\nnsPI8935QE/xeQ/yNeCsKvO9mJEJgnqu47Yr5rttpWmV7neV5leswzWMLLU42nn9aPL1zF/U8Du7\nH/hL4F3AheRrhz8nJ5qurRRflfjvoco1UoV53kWF5AE5kZYoK0FJTiQdAJw01vKMspz/Tcn+Xtbv\n18DCsm5jJgjI5/3riv1mMfm6cr3SDxXG+yWwf9ny1XLNYoKghr9pUQ96Ix2SUtpu6I98EKymk5xN\n/VlE/DAi3jrKsENZxCH3kg84Oxb97h/qkVJ6kpzNLnV/6ZeIeEVEXDfUGAfwGXK2rNRvSj4/VeH7\nVhsQ65gi4kMRMVg0pPYY+el4eWwbo7S+0JM8txy7AV+LiMeK+Q6SLyRqiXs34HVD4xbjH06+SSEi\nXlfSsMvj5BuojV2m0m066vzJ9b8PBO6NiFsi4g2VJhi5ReArI+LBYr+4rCTOXYF7U0prK4y6K/ng\nWm5n4P6U0rMl3e4l35xWWo6dgQdTcdQtGX401bbniN9F2edalO/HQ7G8pMKwoxmv/W20+B8Btq9S\nB3Knov8IKaUHyZn/KytNMCI+WjREtjIiLqgyz6Hp1zTPOpQfr46MiDtK1tU8Rv5+htdxcfyDvJ53\nBn5f0q182rsB7yz73bRReZkkZYcU1zdbACcAt0TEi8m/yc1Z//xfesy8kPz7vTit37hv6XXMSvKT\n5J3LhqllHqMZOiY8sYHjVzPacX60c3Mlpceoe8nLuz1l56TivHo/1c+n97L++qum3mmPOn7J/EvH\nr7aOyl0CLIyIICcevpxSWj1q9M+pel6NiBkRcUZE/LK4vrmnGGdjrsV+l1J6upb5k6sg3Eiu0/9Q\nRHyupH2gcreQExBvLj4PAH9V/N1SZ4y1rvdHqH4+H+o/LKX0TErpm8C+EfH2ShMsuYZYGblhzJrm\nWVzLbF8+zxptSU4sPUAuFTBYdk1ZzUpyKdYhQ5+fqDCs6mSCoA4ppV+klDrIxe3OBK6OiC3JGaty\nD5EPPEOGnoz9hlx8fJehHhHxPHIR/RGzK/t+PvAzcpGhbchFwGPDl6bmWEcVEW8iFwn6O+AFxUXI\n4xsYWy0HhFL3k+sub1fyt0VxI1XLuLeUjbtVSun4ov8V5DqPu6bcQNwFPLdMleJcRX4SOqTSxUTp\neKPOP6X0w5TSweR97RpyvbhKPlNM95XFfnFESZz3A7Or3ITeT26/oNxDwK5ljerNJj+ZqLQcDwMv\nKS4MSoffECN+F+QkxmjKt0P5fjwUS7X9YaL3t9Gm/11y9YW/Le0YEVuRM/wDVcZrofJ2I6X0mWIf\n2iqldFyFQX5OPgG/s2yem5ETUtXmWYvhZY2I3cjFkU8gP3HcjlyMtpZjwsPACyOi9LdUuh/cTy5B\nULoNtkwpnbERsUubhJTSupTSV8k3QG3ki/lnWP/8/yAMN0J7IbmKwPtj/dcWDv82i2PXC8nH4VKj\nzoOxj8MPkY8JpQ2VjXZcL7chx/nRrg0qKT1GzSYv7yOUnZOK8+SujIy9fNzy9VdNLdMebdnrPV9W\nlVL6HvnJ/JuAheQb61qNdl5dCBxMbqtgW3LJBRj9WuxJRr8WKx+n6vyLG+rTU0pzye01vJXcflgl\nQwmCNxWfb2HsBEG9+2a5/wb+tkIjyH9HPtffVWW80a4jtir5u6/KPA8o7n1KvYO8D3y/5uifm+cg\nuSrGP5Krcfwi8puUDq32VoLCneSqoENeBfymQiJTG8AEQR0i4oiI2KHI1D5WdH6WXIz7WfIOPqQP\n+GBE7F6cOD8DXFU8zb0aeFtEvLFoEfQTjH3xvDW5Lu7KiPhzcr3d8TJarGPZmpxM+B3QEhEfZ2RG\nrx6/YeQ6HMsFQE9xQ0JE7BC1v77lOuAVEfHuiNi8+Pt/EdFa9N+a/NTi6Yh4LflENaTS9r4DeHNE\nzI78mrqPbOj8I7+O5fCI2Dal9Ax5uz9bZTpbk7Ooj0d+Vd2HS/r9gHzDdUZEbBkRW0TE3kW//wA+\nFBGviexlxXr8PvkEe0oR03zgbVR5ak2+0V0L/GMx/N+S63VuiC8D7y3WwfPJRShHU76/3EBepwsj\noiUiDiMX97yuxvHHsjH72wgppcfJ1ZXOjYj9i3U3h7wOhhqbpNgPZhefdyM3fLhkA+eZyI0ffaxY\nR1sUTxH/g5z5H27ZPCK2INfHBJhVfK/VUNL0d8W03kt+AllLjPeSG4L9RPE7eAN5/xtyGfnYuV/x\ndGmLiJgfEbtUnKCkYcWx/mByuzaDKb+W9cvk49rWxTHmJPLvDPKDiEQuav3PwKUx8s01B0ZEW3Ed\n8yngeymlEU+ta5jHb4Bdimmsp5jercBni9/7X5BLc15WafgKRp1+BWNdG1RyRETMLc5bnyS31TC0\n3AdFxILIT55PJieGby0Z9x8iv6ruhUA3uVG6WtQy7dHUe74cy6XkxrOfSSnV81rJ0c6rW5OX6VHy\nTf9nysatdA6/g1yaYUZE7E++Qd+g+UdEe0S8stjn/0hO/FS7FrsFaCe39/AA8D/ktoZeRG6ToZJ6\nr0HK/Qs5cdIbES8ufh8d5Gun01JKz0bEn0fEAZHfBrB5RBzBc6UcNsSXyMmHr0R+5ffmkd8m8K/A\nPxfXNhT71BbkqjZD5+qqb41IWX9K6Uhyouvr5KrUDxe/+UouBTqL39525MZPL642j4govZaZWcQ0\nXg9apx0TBPXZH7gzIlaSG+R5V0rpqaI4bA/wncjFlF5PrkfzJXK7BXeTGwfpAkgp3Vl8vpJ8A7eS\n3CroaEWyPkS+SX2C/HSu1pNILarGWoMbycWe/49cPO1p6i8aPuSz5JuXxyLiQzUMfw75Kf9NEfEE\nucGW19Uyo6K44r7k+mIPkYt0DTVcA7mqySeL6X6ckif4lbZ3Sulm8jb5Kbn9h1FPsjXM/93APZGL\n1R1HLuJYyenkem+Pk1tk/mrJPNaRb65eRm7T4AFy/UpSSl8pluEK8j51Dbnl3jXFOAeQb1S/SG61\n+mdVlmMN+Sn4UeTipYeVxlCPoujbvwL95Mz394pe1X4X5wCHRn63978WWeO3ki+UHiWXbHlrSqla\nkbdJ298qSSl9jnwB/nnyNribfBH0lvTcK7vmArdGxCpyA1Y/Jzfut6HzvIq8b32QvL0eBvYC/iql\n9HDJoENvd4BccumpOuaxgtzmyXfJF0CvLGKv1eHk9jMeJddfvIpiHyhuFg4mr7ffkY81H8ZzmTSa\nbxTXLX8kH/ffU1yHQD7XryK3er6MfE5YHBGvId/IH1mcS84kJwtOLZnuFeSGxH5PfvJ3RJX5V5xH\n0W8p+UngryOi2rG6g/z0+CHga+Sbn/+ucdlrmf6wGs7NlXyJfGPya3I1jn8spvVz8jo5l3w+fRv5\nrRBrSsa9gvyGgF+Rq/3V9BrbGqc92vj1ni/H8iVyIrjWxM2Q0c6rl5KvKx8kN7T9vbJxe4G5xTn8\nmqLbieR1MVQ15BpGN9r8X0x+oPdHctWDW6hSOiKl9H/kc+b/FN//SN6m3yl+P5VUir9mxTZsI+9z\nK4r5Xwr8Q0pp6PcV5IeQvyWfM08kN+z3o3rnV8xzNblEx/3kB0pPke8BzmbkKwY/VvQ7lbyfPlV0\nq2UeT6SUelNKbeQHThVf7ZlS+ha5jYZ+8jXuveTjEQARcWdElF47/7yI4yXke5enWL8UjQpDrUar\ngSI/tX+MXH3g7kbHIzWD4onNcnJjR7WUZpnSiiftnyQ3NlqpaN9EzHNf8gXqW1JKd0zGPOsVEVeR\nGxA7bcyBJU2KiLiY3GheTRf901VEDJAbUf2PDRj3HnJDu7UmO5pW5KqyvyW/cekXjY5nUxQR25CT\n8V9LKX18kua5OfBNchLnqORN5bThU5cGiYi3RcTzI9fj+Ty5leB7GhuV1FgR8TdFMbAXkJ/afGNT\nSA4ApJT+k/xk/I2TOM+byK0sv36y5jmWojjvn0bEZkUR0YMZ+ymQJKlxjgd+aHKgcYpSCwcC64rq\ng5Mxz2fI7Q/8kvwGNE0TVeuDaMIdTC6qFOQ6t+8y8ybxPnJRzXXk4nyjvVVk2kkp1dO403jN8xuT\nPc8xvJhcTeVF5Goxx6eUqtXhlCQ1UFESIsivWVYDFdXwTh9zwPGd5+Pk0o+aRqxiIEmSJEmSrGIg\nSZIkSZJMEEiSJEmSJCaoDUS6kJ0AACAASURBVILtt98+zZkzZyImLUnSlHX77bc/klLaodFxbArG\n81pk1apVbLnlluMyrfHUrHFB88ZmXPUxrvoYV32Mqz7jGddo1yMTkiCYM2cOt91220RMWpKkKSsi\n7m10DJuK8bwWGRgYYP78+eMyrfHUrHFB88ZmXPUxrvoYV32Mqz7jGddo1yNWMZAkSZIkSSYIJEmS\nJEmSCQJJkiRJkoQJAkmSJEmShAkCSZIkSZKECQJJkiRJkoQJAkmSJEmShAkCSZIkSZKECQJJkiRJ\nkoQJAkmS1GARsTgifhsRy6v0j4j414i4KyJ+GhF/OdkxqjZ9fX3MmzePBQsWMG/ePPr6+hodUlNr\n1vVlXPXZb7/92GyzzWhvb2ezzTZjv/32a3RIAEQEEUF7e/vw52ZgXPWZ7LhaagzqHuAJYB2wNqW0\n10QGJUmSNikXA+cBl1bpfwDw8uLvdcD5xX81kb6+Prq7u+nt7WXdunXMmDGDzs5OADo6OhocXfNp\n1vVlXPXZb7/9uOmmmzj++OM58MADueGGGzj//PPZb7/9uPHGGxsWV+lNZE9PD93d3cPdU0qNCmtE\nXB/4wAc4++yzh7s3S1wnn3wyX/jCF4a7N0tcp512Gqeffvpw94mKq54SBO0ppVebHJAkSeMppfRt\n4PejDHIwcGnKvgdsFxE7TU50qlVPTw+9vb20t7fT0tJCe3s7vb299PT0NDq0ptSs68u46nPzzTdz\n/PHH88UvfpGtttqKL37xixx//PHcfPPNDY1rSEqJN77xjQ29ya0kpcTBBx/clHG99a1vbcq45s+f\nPylx1VSCQJIkqYFeAtxf8v2BotvDpQNFxLHAsQA77rgjAwMD4zLzlStXjtu0xlOzxTU4OMi6desY\nGBgYjm3dunUMDg42TZzNtM6adX0ZV31SShx44IEj4jrwwAM5//zzG76v9fT0jIhrqCRBo+P6wAc+\nMCKuoZIEjY7r5JNPHhHXUEmCRsd12mmnjYhrqCTBhMWVUhrzD7gb+BFwO3BslWGOBW4Dbps9e3Ya\nT7stum7EnyRJUxFwW6rhvLsp/gFzgOVV+l0HtJV8XwLsNdr0XvOa19S3cUbR398/btMaT80W1x57\n7JGWLl2aUnoutqVLl6Y99tijgVGN1EzrrFnXl3HVJyLS8ccfn1J6Lq7jjz8+RUQDo0oJSPlW77m4\nSrs1inHVZ6LiGu16pNYqBm0ppb8k1wH8h4h4c4VEw4Uppb1SSnvtsMMOteYnJEmSxvIgsGvJ912K\nbmoi3d3ddHZ20t/fz9q1a+nv76ezs3O47rNGatb1ZVz12WeffTj//PN5//vfz8qVK3n/+9/P+eef\nzz777NPQuIZEBLfeemvTNLg3JCL4+te/3pRxXXfddU0Z18DAwKTEVVMVg5TSg8X/30bE14DXAt+e\nyMAkSZIK1wInRMSV5MYJH08pPTzGONNOrReGqUF1Z4caiuvq6mJwcJDW1lZ6enpsoLCKZl1fxlWf\nG2+8kf32248LLriA888/n4hg3333bWgDhZCPA0PHjNIkSqOOD6XzH4prqIHCoe6NVBrXUAOFQ90b\nqTSuoQYKh7pPlDFLEETElhGx9dBnYF+g4muIJEmS6hURfcB3gT+LiAciojMijouI44pBbgB+BdwF\nXAS8v0GhNlR5MdDdFl1XrbpGw3R0dLB8+XKWLFnC8uXLG37z1uyadX0ZV31uvPFGnn32Wfr7+3n2\n2WcbnhwYMnRM6O/vb4rjwxDjqs9kx1VLCYIdga8VmYsW4IqU0rcmNCpJkrTJSCmNepVf1Jf8h0kK\nR5KkTdaYCYKU0q+AV01CLJIkSZIkqUFqbaRQkiRJkiRNYyYIJEmSJEmSCQJJkiRJkmSCQJIkSZIk\nYYJAkiRJkiRhgkCSJEmSJGGCQJIkSZIkYYJAkiRJkiRhgkCSJEmSJGGCQJIkSZIkYYJAkiRJkiQB\nLY0OQJIkSSO96vSbePypZ8Ycbs6p1485zLbP25yfnLbveIQlSZrmTBBIkiQ1mcefeoZ7zjho1GEG\nBgaYP3/+mNOqJYkgSRJYxUCSJEmSJGGCQJIkSZIkYYJAkiRJkiRhGwSSJElNZ+vWU3nlJaeOPeAl\ntUwLYPT2DCRJAhMEkiRJTeeJwTNspFCSNOmsYiBJkiRJkkwQSJIkSZIkEwSSJEmSJAkTBJIkSZIk\nCRMEkiRJkiQJEwSSJEmSJAkTBJIkSZIkCRMEkiRJkiQJEwSSJEmSJAkTBJIkSZIkCRMEkiRJkiQJ\nEwSSJEmSJAkTBJIkSZIkCRMEkiRJkiQJEwSSJEmSJAkTBJIkSZIkCWhpdACSJEla35xTrx97oG+N\nPcy2z9t8HKKRJG0KTBBIkiQ1mXvOOGjMYeacen1Nw0mSVCurGEiSJEmSJBMEkiRJkiTJBIEkSZIk\nScIEgSRJkiRJwgSBJEmSJEnCBIEkSZIkScIEgSRJkiRJwgSBJEmSJEnCBIEkSZIkScIEgSRJkiRJ\nwgSBJEmSJEnCBIEkSZIkScIEgSRJkiRJwgSBJEmSJEnCBIEkSWqwiNg/In4eEXdFxKkV+s+OiP6I\n+HFE/DQiDmxEnJIkTXcmCCRJUsNExAzg34ADgLlAR0TMLRvsY8CXU0p7Au8Cvji5UUqStGkwQSBJ\nkhrptcBdKaVfpZTWAFcCB5cNk4Btis/bAg9NYnySJG0yIqVU24A5w38b8GBK6a2jDbvXXnul2267\nbRzCy+acev2I7/eccdC4TVuSpMkSEbenlPZqdBzNJCIOBfZPKf198f3dwOtSSieUDLMTcBPwAmBL\n4C0ppdsrTOtY4FiAHXfc8TVXXnnluMS4cuVKttpqq3GZ1ng66luruHj/LRsdRkXNus6Mqz7GVR/j\nqo9x1Wc842pvb696PdJSx3ROBAZ5LoMvSZI0GTqAi1NKX4iINwBfioh5KaVnSwdKKV0IXAj5YcX8\n+fPHZeYDAwOM17TG1beub864aN51Zlz1Ma76GFd9jKs+kxVXTVUMImIX4CDgPyY2HEmStIl5ENi1\n5PsuRbdSncCXAVJK3wW2ALaflOgkSdqE1NoGwdnAKcCz1QaIiGMj4raIuO13v/vduAQnSZKmvR8C\nL4+I3SNiJrkRwmvLhrkPWAAQEa3kBIEXG5IkjbMxEwQR8Vbgt5Xq+pVKKV2YUtorpbTXDjvsMG4B\nSpKk6SultBY4AbiRXJXxyymlOyPikxHx9mKwk4FjIuInQB9wVKq1ESVJklSzWtog2Bt4e/HO4S2A\nbSLispTSERMbmiRJ2hSklG4Abijr9vGSzyvI1yOSJGkCjVmCIKX0kZTSLimlOeRif0tNDkiSJEmS\nNL3U2gaBJEmSJEmaxup5zSEppQFgYEIikSRJkiRJDWMJAkmSJEmSZIJAkiRJkiSZIJAkSZIkSZgg\nkCRJkiRJ1NlIoSRJkhojItbvdub6w6WUJiEaSdJ0ZAkCSZKkKSClxBVXXMHuu+/O0qVLufnmm1m6\ndCm77747V1xxBSklkwOSpI1igkCSJGmK6Onpobe3l/b2dlpaWmhvb6e3t5eenp5GhyZJmgZMEEiS\nJE0Rg4ODtLW1jejW1tbG4OBggyKSJE0nJggkSZKmiNbWVpYtWzai27Jly2htbW1QRJKk6cQEgSRJ\n0hTR3d1NZ2cn/f39rF27lv7+fjo7O+nu7m50aJKkacC3GEiSJE0RHR0d3HrrrRxwwAGsXr2aWbNm\nccwxx9DR0dHo0CRJ04AlCCRJkqaIvr4+rrrqKnbaaSc222wzdtppJ6666ir6+voaHZokaRowQSBJ\nkjRFnHLKKbS0tLB48WJuvPFGFi9eTEtLC6ecckqjQ5MkTQMmCCRJkqaIBx54gEsuuWTEaw4vueQS\nHnjggUaHJkmaBkwQSJIkSZIkEwSSJElTxS677MKRRx454i0GRx55JLvsskujQ5MkTQO+xUCSJGmK\n+NznPseJJ57I0Ucfzb333stuu+3GunXrOOussxodmiRpGrAEgSRJ0hTR0dHBOeecw5ZbbklEsOWW\nW3LOOef4mkNJ0rgwQSBJkjSF3Hrrrdx11108++yz3HXXXdx6662NDkmSNE1YxUCSJGmK6Orq4oIL\nLuDMM89k7ty5rFixgkWLFgFw7rnnNjg6SdJUZwkCSZKkKeKiiy7izDPP5KSTTmKLLbbgpJNO4swz\nz+Siiy5qdGiSpGnABIEkSdIUsXr1al7wghcwb948FixYwLx583jBC17A6tWrGx2aJGkasIqBJEnS\nFNHS0sKHPvQhrr76atatW8eMGTM49NBDaWnxkk6StPEsQSBJkjRFbLPNNjz++OP8+Mc/Zu3atfz4\nxz/m8ccfZ5tttml0aJKkacB0syRJ0hTx2GOP8b73vY+PfvSjrF69mlmzZnHsscfy7//+740OTZI0\nDViCQJIkaYpobW3lne98J08//TT9/f08/fTTvPOd76S1tbXRoQHQ19c3on2Evr6+RocENG9cktRs\nLEEgSZI0RXR3d9PZ2Ulvby/r1q2jv7+fzs5Oenp6Gh0afX19dHd3D8c2Y8YMOjs7Aejo6DAuSZoC\nLEEgSZI0RXR0dNDT00NXVxf77bcfXV1d9PT0NMWNbk9PD729vbS3t9PS0kJ7ezu9vb0NT140a1yS\n1IxMEEiSJGmjDQ4O0tbWNqJbW1sbg4ODDYooa9a4JKkZWcVAkiRpimjm4vKtra0sW7aM9vb24W7L\nli1rePsIzRqXJDUjSxBIkiRNET09PSxcuHBEFYOFCxc2RXH5ofYR+vv7Wbt27XD7CN3d3cYlSVOE\nJQgkSZKmiBUrVrBq1SoWL148XILg6KOP5t577210aMMlGLq6uhgcHKS1tbUp2kdo1rgkqRlZgkCS\nJGmKmDlzJl1dXSMa3Ovq6mLmzJmNDg3IN+PLly9nyZIlLF++vGluwps1LklqNpYgkCRJmiLWrFnD\neeedx5577jn8msPzzjuPNWvWNDo0SdI0YIJAkiRpipg7dy6HHHLIiOLyCxcu5Jprrml0aJKkacAE\ngSRJ0hTR3d1d8S0GzdBIoSRp6jNBIEmSNEXY4J4kaSKZIJAkSZpCOjo66OjoYGBggPnz5zc6HEnS\nNOJbDCRJkiRJkgkCSZIkSZJkgkCSJEmSJGGCQJIkSZIkYYJAkiRJkiRhgkCSJEmSJGGCQJIkSZIk\nYYJAkiRJkiRhgkCSJEmSJGGCQJIkNVhE7B8RP4+IuyLi1CrD/F1ErIiIOyPiismOUZKkTUFLowOQ\nJEmbroiYAfwbsA/wAPDDiLg2pbSiZJiXAx8B9k4p/SEi/qQx0UqSNL1ZgkCSJDXSa4G7Ukq/Simt\nAa4EDi4b5hjg31JKfwBIKf12kmOUJGmTYAkCSZLUSC8B7i/5/gDwurJhXgEQEd8BZgCfSCl9q3xC\nEXEscCzAjjvuyMDAwLgEuHLlynGb1nhYsmQJl112Gffddx+zZ8/miCOOYMGCBY0Oa4RmW2dDjKs+\nxlUf46qPcdVnsuIyQSBJkppdC/ByYD6wC/DtiHhlSumx0oFSShcCFwLstddeaf78+eMy84GBAcZr\nWhurr6+Pyy+/nMWLF7Nu3TpmzJhBZ2cnc+fOpaOjo9HhDWumdVbKuOpjXPUxrvoYV30mKy6rGEiS\npEZ6ENi15PsuRbdSDwDXppSeSSndDfwfOWGwyenp6aG3t5f29nZaWlpob2+nt7eXnp6eRocmSZoG\nxkwQRMQWEfGDiPhJ0XLw6ZMRmCRJ2iT8EHh5ROweETOBdwHXlg1zDbn0ABGxPbnKwa8mM8hmMTg4\nSFtb24hubW1tDA4ONigiSdJ0UksJgtXAX6eUXgW8Gtg/Il4/sWFJkqRNQUppLXACcCMwCHw5pXRn\nRHwyIt5eDHYj8GhErAD6gQ+nlB5tTMSN1drayrJly0Z0W7ZsGa2trQ2KSJI0nYzZBkFKKQEri6+b\nF39pIoOSJEmbjpTSDcANZd0+XvI5AScVf5u07u5uOjs76e3tZd26dfT399PZ2WkVA0nSuKipkcLi\nHcW3Ay8jv2bo+xWGGW45ePbs2eMZ47Q159TrR3y/54yD6h6m3v61qBRHs6tlXU7GNCRJmkgdHR1c\nfPHFLFiwgJQSEcE+++zTVA0USpKmrpoaKUwprUspvZrccNBrI2JehWEuTCntlVLaa4cddhjvOCVJ\nkjZ5XV1dLF26lM9//vN885vf5POf/zxLly6lq6ur0aFJkqaBut5iULxOqB/Yf2LCkSRJUjUXXXQR\nZ555JieddBJbbLEFJ510EmeeeSYXXXRRo0OTJE0DtbzFYIeI2K74/DxgH+BnEx2YJEmSRlq9ejXH\nHXfciG7HHXccq1evblBEkqTppJYSBDsB/RHxU/KriG5OKV03sWFJkiSp3KxZs7jgggtGdLvggguY\nNWtWgyKSJE0ntbzF4KfAnpMQiyRJkkZxzDHHsGjRIgDmzp3LWWedxaJFi9YrVSBJ0oao6S0GkiRJ\narxzzz0XgI9+9KOsXr2aWbNmcdxxxw13lyRpY9TVSKEkSZIa69xzz+Xpp5+mv7+fp59+2uSAJGnc\nmCCQJEmSJEkmCCRJkiRJkgkCSZIkSZKECQJJkiRJkoQJAkmSJEmShAkCSZIkSZKECQJJkiRJkoQJ\nAkmSJEmShAkCSZIkSZKECQJJkiRJkoQJAkmSJEmShAkCSZIkSZKECQJJkiRNc319fcybN48FCxYw\nb948+vr6Gh2SJDWllkYHIEmSJE2Uvr4+uru76e3tZd26dcyYMYPOzk4AOjo6GhydJDUXSxBIkiRp\n2urp6aG3t5f29nZaWlpob2+nt7eXnp6eRocmSU3HBIEkSZKmrcHBQdra2kZ0a2trY3BwsEERSVLz\nMkEgSZKkaau1tZVly5aN6LZs2TJaW1sbFJEkNS8TBJIkSZq2uru76ezspL+/n7Vr19Lf309nZyfd\n3d2NDk2Smo6NFEqSJGnaGmqIsKuri8HBQVpbW+np6bGBQkmqwBIEkiRJU4iv7KtfR0cHy5cvZ8mS\nJSxfvtzkgCRVYQkCSZKkKcJX9kmSJpIlCCRJkqaIZnxlX0Ss99fe3r5eN0lS8zNBIEmSNEU04yv7\nUkrr/e226Lr1ujWS1TIkqTZWMZAkSZoihl7Z197ePtzNV/aNzmoZklQ7EwSSJElTRHd3N4cddhhb\nbrkl9913H7Nnz2bVqlWcc845jQ6taZVWyxgYGGD+/Pn09vbS1dVlgkCSyljFQJIkaQpqdLH9qaIZ\nq2VIUrMyQSBJkjRF9PT0cNVVV3H33XezdOlS7r77bq666qqGNlLY7IaqZZSyWoYkVWaCQJIkaYrw\naXj9uru76ezspL+/n7Vr19Lf309nZyfd3d2NDk11srFJaeLZBoEkSdIUYSOF9RtqZ6Crq4vBwUFa\nW1vp6emx/YEpxsYmpclhCQJJkqQpwqfhG6ajo4Ply5ezZMkSli9f7g3lFFTa2GRLSwvt7e309vZa\nvUYaZ5YgkCRJmiJ8Gq5NldVrpMlhCQJJkiRJTc3GJqXJYQkCSZKkKcJ62NpUDVWvGdr3h6rXWMVA\nGl8mCCRJkqaI0nrYAwMDzJ8/n97eXrq6ukwQaFqzeo00OUwQSJIkTRGNrof9qtNv4vGnnqlp2Dmn\nXj9q/22ftzk/OW3f8QhLm4iOjg46OjqGk2OSxp8JAkmSpCmi0a85fPypZ7jnjIPGHK6WG7ixEgiS\npMlnI4WSJElThK85lCRNJEsQSJIkTRHWw5YkTSQTBJIkSVOI9bAlSRPFKgaSJEmSJMkEgSRJkiRJ\nMkEgSZIkSZIwQSBJkhosIvaPiJ9HxF0Rceoow70jIlJE7DWZ8UmStKkwQSBJkhomImYA/wYcAMwF\nOiJiboXhtgZOBL4/uRFKkrTp8C0GkiSpkV4L3JVS+hVARFwJHAysKBvuU8CZwIcnNzyV2rr1VF55\nSdVCHiNdMta0AA7a2JC0Cenr66Onp2f4FZ/d3d2+4lMaZyYIJElSI70EuL/k+wPA60oHiIi/BHZN\nKV0fEVUTBBFxLHAswI477sjAwMC4BLhy5cpxm9Z4akRcTwyewcX7bznmcCtXrmSrrbYadZijvrVq\n0uN3W9anmeJasmQJvb29fPjDH2b33Xfn7rvv5uSTT2bFihUsWLCg0eEBzbW+ShlXfTb1uEwQSJKk\nphURmwFnAUeNNWxK6ULgQoC99torzZ8/f6Pm3exPKwcGBtjYZazbt66vaZ41xVbjtMaD23LDNFNc\nJ5xwApdffjnt7e0MDAzwwQ9+kFe/+tV0dXXxqU99qtHhAc21vkoZV3029bhMEEiSpEZ6ENi15Psu\nRbchWwPzgIGIAHgxcG1EvD2ldNtEBdXX10d3dze9vb2sW7eOGTNm0NnZCdBUN5Yam9tyehgcHKSt\nrW1Et7a2NgYHBxsUkTQ92UihJElqpB8CL4+I3SNiJvAu4Nqhnimlx1NK26eU5qSU5gDfAyY0OQDQ\n09PDwoUL6erqYr/99qOrq4uFCxfS09MzkbPVBOjp6aG3t5f29nZaWlpob2+nt7fXbTnFtLa2smzZ\nshHdli1bRmtra4MikqYnSxBIkqSGSSmtjYgTgBuBGcDilNKdEfFJ4LaU0rWjT2FirFixglWrVrF4\n8eLhp85HH3009957byPC0UbwyfP00N3dTWdn53BJkP7+fjo7O030SOPMBIEkSWqolNINwA1l3T5e\nZdj5kxHTzJkz6erqGq7vPH/+fLq6uvjoRz86GbPXOBp68tze3j7czSfPU89QdZCurq7htiR6enqs\nJiKNszGrGETErhHRHxErIuLOiDhxMgKTJElqlDVr1nDeeefR39/P2rVr6e/v57zzzmPNmjWNDk11\nGnryXLotOzs76e7ubnRoqlNHRwfLly9nyZIlLF++3OSANAFqKUGwFjg5pfSjiNgauD0ibk4plb+f\nWJIkaVqYO3cuhxxyyIinlQsXLuSaa65pdGiqk0+eJal2YyYIUkoPAw8Xn5+IiEHyO4tNEEiSpGmp\nu7u7Ysv31neemjo6Oujo6Gja15dJUrOoqw2CiJgD7Al8fyKCkSRJagY+dZ5e+vr66OnpGd6W3d3d\nbktJqqDmBEFEbAX8F/CBlNIfK/Q/FjgWYPbs2eMW4Iaac+r1I77fc8ZBDYrkOeUxTRUTEXf59mjG\n7SVJ2rT51Hl66Ovrq1gaBDBJIEllxmykECAiNicnBy5PKX210jAppQtTSnullPbaYYcdxjNGSZIk\naYP09PTQ29tLe3s7LS0ttLe309vba3URSaqglrcYBNALDKaUzpr4kCRJkqTxMTg4SFtb24hubW1t\nDA4ONigiSWpetZQg2Bt4N/DXEXFH8XfgBMclSZKkCvr6+pg3bx4LFixg3rx59PX1NTqkptba2sqy\nZctGdFu2bBmtra0NikiSmlctbzFYBsQkxCJJkqRRWJ++ft3d3XR2dg6vs/7+ft9IIUlV1PUWA0mS\nJDVOaX36ocYTe3t76erqMkFQhW+kkKTa1dRIoSRJ0qamGYvyW59+w3R0dLB8+XKWLFnC8uXLmyY5\n0Iz7mKRNmyUIJEmSyjRrUf6h+vTt7e3D3axPPzU16z4madNmgkCSJKlMsxblb4b69HNOvb62Ab81\n+nDbPm/zcYhm6mrWfUzSps0EgSRJUplmLcrf6Pr095xxUE3DzTn1+pqH3VQ16z4GuXRDT0/P8D7W\n3d1t0kLaRJggkCRJKtPMRfk7Ojro6OgYfuqsqalZ9zGrPkibNhsplCRJKjNUlL+/v5+1a9cOF+Xv\n7u5udGiaJpp1Hyut+tDS0kJ7ezu9vb2+FlLaRFiCQJIkqUyji/Jr+mvWfayZqz5ImniWIJAkSZIa\noBlfvzhU9aFUM1R9kDQ5LEEgSZJUxnrY2lQ1w5syJDWOCQJJkqQyPT09LFy4cETx74ULFzZFEXBp\nIjVr1QdJk8MEgSRJUpkVK1awatUqFi9ePFyC4Oijj+bee+9tdGjShPNNGdKmywSBJElSmZkzZ7L3\n3nuPeIq699578/DDDzc6NEmSJowJAkmSpDKrV6/mqquu4swzz2Tu3LmsWLGCRYsWsXbt2kaHJknS\nhDFBIEmSVGbWrFkceuihLF68eLgEwWGHHcbVV1/d6NAkSZowvuZQkiSpzJo1a/jOd77Dueeey403\n3si5557Ld77zHdasWdPo0LQB+vr6mDdvHgsWLGDevHn09fU1OiRJakqWIJAkSSozd+5cDjnkkBFt\nEBx++OFcc801jQ5NdfKVlZJUOxMEkiRJZbq7uyveVPou+Kmnp6eH3t5e2tvbh1vl7+3tpaurywSB\nJJUxQSBJklSmo6ODW2+9lQMOOIDVq1cza9YsjjnmGG8op6DBwUEeeOAB5s2bN1waZNGiRQwODjY6\nNElqOiYIJEmSyvT19XH99dfzzW9+c0QJgje+8Y0mCaaYnXfemUWLFnH55ZcPb8vDDz+cnXfeudGh\nSVLTsZFCSZKkMqXF0ltaWmhvb6e3t9cqBlPUk08+ydFHH82+++7L0UcfzZNPPtnokCSpKZkgkCRJ\nKjM4OEhbW9uIbm1tbRZLn4IefPBBNt98cwAiAoDNN9+cBx98sJFhSVJTMkEgSZJUprW1lWXLlo3o\ntmzZMlpbWxsU0XN8ZV99Zs6cyUc+8hHuvvtulixZwt13381HPvIRZs6c2ejQJKnp2AaBJElSme7u\nbjo7O4ffYtDf398UbzHwlX31W7NmDeeddx577rnn8LY877zzWLNmTaNDk6SmY4JAkiSpzNDNdldX\n13DL9z09PQ2/CfeVffWbO3cuhxxyyIhtuXDhQq655ppGhyZJTccEgSRJUgUdHR10dHQM34g3A9tG\nqF93d3fFUheNLg0iSc3IBIEkSdIUMdQ2Qnt7+3C3ZmkboVk1a2kQSWpGNlIoSZI0RQy1jdDf38/a\ntWuH20bo7u5udGhNraOjg+XLl7NkyRKWL19uckCSqrAEgSRJ0hTh03BJ/5+9O4+To6z2P/75JpCw\nhMXIprIkAmJCICwBssPofgAAIABJREFUBOKVgKyKooAQUFFGEdAQb66XgNEfgowYvOACyJogKERF\nBJFNFCZIomxhDYwogkiQxZUlCyTh/P54qic9nVlhup+a5Pt+vfqVruqarpOa7p6uU89zjlk9OUFg\nZmZm1o+UsTaCmZmtGDzFwMzMzMzM2syYMYNRo0ax1157MWrUKGbMmJE7JDNrEI8gMDMzMzMzICUH\nOur6AHgqi9lKwCMIzMzMzMwMgObmZqZNm8a4ceNYZZVVGDduHNOmTXNbSLOVhBMEZmZmZmYGQGtr\nK2PHjm23buzYsbS2tmaKyMwayQkCMzMzMzMDYMSIEcyaNavdulmzZjFixIhMEZlZIzlBYGZmZmZm\nAEyZMoWmpiZaWlpYsmQJLS0tNDU1MWXKlNyhmVkDuEihmZmZmZkBywoRTpgwgdbWVkaMGEFzc7ML\nFJqtJJwgMDMzMzOzNuPHj2f8+PHMnDmTPfbYI3c4ZtZAnmJgZmZmZmZmZh5BYGZmZmYrHkk92i4i\n6hyJmVn/4REEZmZmZrbCiYjlbptNvn65dWZmtowTBGZmZpaVpP0kPSbpcUkndfD4JEmPSnpI0q2S\nNmtEXDNmzGDUqFHstddejBo1ihkzZjRit2ZmZtl4ioGZmZllI2kgcB6wNzAPuEfSdRHxaNVm9wNj\nImKBpOOAM4HD6hnXjBkzmDJlCtOmTWPp0qUMHDiQpqYmAFdzNzOzFZYTBGZmZpbTzsDjEfEEgKQf\nAx8G2hIEEdFStf2dwMfrHVRzczNHHHFEu1ZvRxxxhNu9daCzuf6a2n7Zw/nNzMrPCQIzMzPL6R3A\n01XL84Bduti+CbiprhEBjz76KPPnz2f69OltIwiOPvponnrqqXrvut/p6MTf7fHMzPonJwjMzMys\nX5D0cWAM8L5OHj8GOAZgww03ZObMmW94X6ussgr7778/kli0aBFDhgxh//3355JLLnlTz9uXXnnl\nldLEUqvMsZUxrrIeL8fVO46rdxxX7zQqLicIzMzMLKdngE2qljcu1rUj6f3AFOB9EfFqR08UERcB\nFwGMGTMm3swV7CVLlnDzzTdz6KGHstpqqxER3HzzzSxZsqQ0V8bLfJW+tLHdfEMp4yrr8XJcveO4\nesdx9U6j4nIXAzMzM8vpHmBLScMlDQIOB66r3kDS9sCFwIci4oVGBDVy5EhGjx7N/vvvz957783+\n++/P6NGjGTlyZCN2bysJd8ows7LxCAIzMzPLJiKWSPoC8CtgIDA9Ih6RdBpwb0RcB3wLGAJcVRTE\n+2tEfKiecY0bN44LLriAqVOnMnLkSB599FEmT57MscceW8/d2krEnTLMrIycIDAzM7OsIuJG4Maa\ndf+v6v77Gx1TS0sLkydPZvr06W1dDCZPnsy1117b6FBsBdXc3My0adMYN25c29DhadOmMWHCBCcI\nzCwbJwjMzMzMarS2tnL//fdz+umnt528LV68mDPOOCN3aLaCaG1tZezYse3WjR07ltbW1kwRmZm5\nBoGZmZnZckaMGMGsWbParZs1axYjRozIFJGtaPwaM7MycoLAzMzMrMaUKVNoamqipaWFJUuW0NLS\nQlNTE1OmTMkdmq0gyvwac/FEs5VXt1MMJE0HPgi8EBGj6h+SmZmZWV6VOeATJkxoq0HQ3NzsueHW\nZ8r6GnPxRLOVW09GEPwA2K/OcZiZmZmVyvjx45k7dy633norc+fO9cmR9bkyvsaqiyeussoqjBs3\njmnTptHc3Jw7NDNrgG4TBBHxW+BfDYjFzMzMrDQ8zNpWRi6eaLZycw0CMzMzsxozZsxg4sSJzJ8/\nH4D58+czceJEJwm64aRK/+fiiWYrtz5rcyjpGOAYgE033bSvnrZDw066Ybl1f/nmB97Uc9T+fEf7\n6E5fPEd36vGcOfbR2xje7O+3I909Z18ch97GXQ9v9liarSz8XrFqJ554IqussgrTp09vm4d95JFH\ncuKJJ5ZiGHgZee76iqFSPLHye6wUT/QUA7OVQ5+NIIiIiyJiTESMWX/99fvqac3MzMwabt68eVx2\n2WXt5mFfdtllzJs3L3dopeW56yuG8ePH09zczIQJE9h3332ZMGFCKYonmllj9NkIAjMzMzNbeXnu\n+opj/PjxjB8/npkzZ7LHHnvkDsfMGqjbEQSSZgC/B7aSNE9SU/3DMjMzM8tn44035pOf/GS7HvWf\n/OQn2XjjjXOHVlqeu77icC0Js5VXtyMIIsLjiczMzGylcuaZZzJx4kSOPvponnrqKTbbbDOWLl3K\n2WefnTu00vLc9RVDpUDnmmuuCSwr0AmuJWG2MvAUAzMzM7MalROh5uZmJLHmmmvyjW98wydIXagc\nmwkTJtDa2sqIESM8d70fOvHEE1m8eDEAEQHA4sWLXaDTbCXhBIGZmZlZBzwPu/d8zPq/efPmsdFG\nG7Xr4HHEEUe4QKfZSqLPuhiYmZmZmVnPlXWu/6RJk9p1o5g0aVLukMysQTyCwMzMzMyswWbMmMGU\nKVPaajYMHDiQpqZUCzz3UP6zzjqLMWPGtNWSOOuss7LGY2aN4xEEZmZmZmYN1tzczLRp09pdqZ82\nbVr2oo4bb7wxL7/8Mvvuuy977703++67Ly+//LI7eJitJJwgMDMzM6siqd1t3Lhxy62TlDtM6+da\nW1sZO3Zsu3Vjx46ltbU1U0TJQQcdxKJFixg6dCiSGDp0KIsWLeKggw7KGpeZNYYTBGZmZmZVIqLd\nbbPJ1y+3rlLd3eyNGjFiBLNmzWq3btasWYwYMSJTRElLSwsnn3wy6623HpJYb731OPnkk2lpacka\nl5k1hhMEZmZmZv1IWQvbWe9MmTKFww47jOHDh7PnnnsyfPhwDjvsMKZMmZI1rtbWVk455RTmzp3L\nrbfeyty5cznllFOyj2wws8ZwkUIzMzOzfqLMhe3sjSvTlJURI0Zw6qmncu2119La2sqIESM46KCD\nso9sMLPG8AgCMzMzs36irIXtrPeam5vZfffdefbZZ3n99dd59tln2X333bP/LseNG8fUqVM5+uij\nueGGGzj66KOZOnUq48aNyxqXmTWGRxCYmZmZ9RNlLWyX2+hTb+HFhYt7tO2wk27o8vF1Vl+VB0/Z\npy/C6tIjjzzCY489xtSpUxk5ciSPPvookydPZsmSJXXfd1daWlqYPHky06dPbxtBMHnyZK699tqs\ncZlZYzhBYGZmZtZPVArbVV/NLUNhu4oZM2bQ3NzcdmI5ZcqUhkx9eHHhYv7yzQ90u93MmTPZY489\nutymuwRCX5HEZz/7WSZNmsTMmTOZNGkSjz/+OBdccEFD9t+Z1tZW7r//fk4//fS247V48WLOOOOM\nrHGZWWM4QWBmZmbWT0yZMoWmpqa2GgQtLS00NTVlH5YOro/QWxHBTTfdREtLS9vv8qabbsreIaPs\nSSgzqy8nCMzMzMz6icqJ9oQJE9qu0jc3N5fiBLy6PkLlyvO0adOYMGFCKeIrm8GDB7No0SL23HPP\ntnUbbbQRgwcPzhJPdaHE6phqt8mdwDCz+nKRQjMzM7N+ZPz48e1a0JXl5Nv1EXpngw024LnnnmO3\n3XbjqquuYrfdduO5555jgw02yBJPRLTdrrzySrbeemvQALbeemuuvPLKtsfMbMXmBIGZmZmZvWmV\noenVPDS9c/PmzWPrrbdmzpw5HHroocyZM4ett96aefPm5Q6tLQm12YnXlSoJNWPGDEaNGsVee+3F\nqFGjmDFjRu6QzFY4ThCYmZmZ2ZtWqY/Q0tLCkiVL2uojTJkyJXdopRQRzJ49m0WLFtHS0sKiRYuY\nPXu2r9J3YsaMGUycOJH58+cDMH/+fCZOnOgkgVkfcw0CMzMzM3vTylwfoYwkcfLJJ/P973+/bd3J\nJ5/crhaALXPiiSeyeHFqZVlJoixevJgTTzzRrzGzPuQEgZmZmZn1ifHjxzN+/PgetRNc2e29996c\nf/75ABxwwAEcf/zxnH/++eyzzz6ZIyunefPmsc4667RbFxGlmJJhtiJxgsDMzMzMrEFqRwicf/75\nbYkCgFtuucXdAjqxcOFCFixYQETwzDPPeLSFWR24BoGZmZmZWYNUdwuo3DabfP1y62x5r732GkOG\nDEESQ4YM4bXXXssdktkKxwkCMzMzMzMrPUkMGjSIiGDQoEEeQWBWB55iYGZmZiut0afewosLF3e7\n3bCTbuh2m3VWX5UHT/H8cbN6WWONNVh99dUZMGAAq6++OmussUZbVwMz6xtOEJiZmdlK68WFi/nL\nNz/Q5TY9LbjXkySCmZlZmXmKgZmZmZmZldrQoUNZsGABCxcuJCLaChYOHTo0d2hmKxQnCMzMzMzM\nrNTOPfdcBg8ezPPPP09E8PzzzzN48GDOPffc3KGZrVCcIDAzMzMzs9Jba621GDZsGJIYNmwYa621\nVu6QzFY4ThCYmZmZmVmpNTc385Of/IQnn3yS2267jSeffJKf/OQnNDc35w7NbIXiIoVmZmZm1q+t\nNeIktrnspJ5tfFl3zwXQdeHK/q6n3Tug++Kbjere0draytixY9utGzt2LK2trXXft9nKxAkCMzMz\nM+vXXm79ZrfdKKBnHSlWhm4UPeneAeU6XiNGjGDWrFmMGzeubd2sWbMYMWJEQ/ZvtrJwgsDMzMzM\nrA7645X6spoyZQpNTU1MmzaNpUuX0tLSQlNTk6cYmPUxJwjMzMzMzOqgP16pLxtJ7Zb33HPPdstH\nHHEERxxxBBHRyLDMVlguUmhmZmZmZqUUEcvdNpt8/XLrzKxveASBmZmZrbR6XNyum8J26blgRS9u\nZ2ZmKzYnCMzMzGyl1ZPidj0Z/g0r7xBwMzNbcThBYGZmZmb9Xo8TNDd3XwzQzGxl5QSBmZmZmfVr\nPSkECCmJ0NNtV2Q9nloD3U6v6cupNe76YJafEwRmZmZmZiuRnkytgcZ3V3DXB7P8nCAwMzOzlVqP\nTiS6GZYOHppuyyvrlfqy8vEyy88JAjMzM8tK0n7Ad4GBwCUR8c2axwcDlwM7Av8EDouIv/TFvnty\ntdLD0u2NKuuV+l49XwNrNpT5eJmtLJwgMDMzs2wkDQTOA/YG5gH3SLouIh6t2qwJ+HdEbCHpcGAq\ncFjjozXrvTKeiJe5ZkMZj5fZysQJAjMzM8tpZ+DxiHgCQNKPgQ8D1QmCDwNfK+7/DDhXkiIiGhmo\nWW+V+US8jDo6BpJ69LP+ODDrG04QmJmZWU7vAJ6uWp4H7NLZNhGxRNKLwFuBf1RvJOkY4BiADTfc\nkJkzZ76hgMaNG7fcOk1dfruWlpY39Px95ZVXXnnD/8d6K0NsHf0eYfnfZaN/j46rdzra3yuvvMKQ\nIUParavn623CUxN6vnE3tREAztnsnDceTBXH1TuOq2ecIDAzM7MVQkRcBFwEMGbMmOhujnIXz9Nu\nuSfznXMoa1xQjtg6uqLsuDpX1rg60ui4HubhHm3nuBLH1Ttli2tA3fdgZmZm1rlngE2qljcu1nW4\njaRVgHVIxQrNzMysDzlBYGZmZjndA2wpabikQcDhwHU121wHHFXcPwS4zfUHzMzM+p6nGJiZmVk2\nRU2BLwC/IrU5nB4Rj0g6Dbg3Iq4DpgE/lPQ48C9SEsHMzMz6mBMEZmZmllVE3AjcWLPu/1XdXwQc\n2ui4zMzMVjaeYmBmZmZmZmZmThCYmZmZmZmZmRMEZmZmZmZmZoYTBGZmZmZmZmaGEwRmZmZmZmZm\nhhMEZmZmZmZmZkYPEwSS9pP0mKTHJZ1U76DMzMzMzMzMrLG6TRBIGgicB+wPjATGSxpZ78DMzMzM\nzMzMrHF6MoJgZ+DxiHgiIl4Dfgx8uL5hmZmZmZmZmVkjKSK63kA6BNgvIj5TLH8C2CUivlCz3THA\nMcXiVsBjfRjnesA/+vD5VmY+ln3Hx7Lv+Fj2HR/LvlOPY7lZRKzfx89pHZD0d+CpPnq6sr6vyhoX\nlDc2x9U7jqt3HFfvOK7e6cu4Ov0+skof7YCIuAi4qK+er5qkeyNiTD2ee2XjY9l3fCz7jo9l3/Gx\n7Ds+lv1bXyZiyvpaKGtcUN7YHFfvOK7ecVy947h6p1Fx9WSKwTPAJlXLGxfrzMzMzMzMzGwF0ZME\nwT3AlpKGSxoEHA5cV9+wzMzMzMzMzKyRup1iEBFLJH0B+BUwEJgeEY/UPbL26jJ1YSXlY9l3fCz7\njo9l3/Gx7Ds+llZR1tdCWeOC8sbmuHrHcfWO4+odx9U7DYmr2yKFZmZmZmZmZrbi68kUAzMzMzMz\nMzNbwTlBYGZmZmZmZmblShBI2k/SY5Iel3RSB48PlvST4vG7JA1rfJT9Qw+O5SRJj0p6SNKtkjbL\nEWd/0N2xrNruYEkhqXRtUcqiJ8dS0seK1+Yjkq5sdIz9RQ/e45tKapF0f/E+PyBHnP2BpOmSXpA0\nt5PHJel7xbF+SNIOjY7RzMzMrBFKU4NA0kDgj8DewDxS94TxEfFo1TbHA9tGxLGSDgc+EhGHZQm4\nxHp4LMcBd0XEAknHAXv4WC6vJ8ey2G4t4AZgEPCFiLi30bGWXQ9fl1sCPwX2jIh/S9ogIl7IEnCJ\n9fBYXgTcHxHnSxoJ3BgRw3LEW3aS/gt4Bbg8IkZ18PgBwATgAGAX4LsRsUtjozQrP0mrA5tGxGO5\nY6kmafeImN3dOrMVhaTBEfFqd+tWdpJ+CXR6MhwRH2pgOKVRphEEOwOPR8QTEfEa8GPgwzXbfBi4\nrLj/M2AvSWpgjP1Ft8cyIloiYkGxeCewcYNj7C968roE+DowFVjUyOD6mZ4cy88C50XEvwGcHOhU\nT45lAGsX99cB/tbA+PqViPgt8K8uNvkwKXkQEXEnsK6ktzUmOstF0lmSts4dRy1J60v6sqSLitEv\n0yVNL0FcBwIPADcXy9tJKktb7HN6uK6hJE2UtHYxSmmapPsk7VOCuDaXNLi4v4ekEyStW4K4ziyO\n16rF6Ne/S/p4CeLasPj93VQsj5TUlDms3/dwXcMVn2H/J+lGSbdVbpnC+T/grC5u2Uk6tLgQiaSv\nSPp5vUcylilB8A7g6arlecW6DreJiCXAi8BbGxJd/9KTY1mtCbiprhH1X90ey+JNuklE3NDIwPqh\nnrwu3wW8S9JsSXdK2q9h0fUvPTmWXwM+LmkecCPpCri9Mb39TLUVQytwUTGl8VhJ6+QOqPALUtLv\nN6SRa5Vbbl8jJS//AxARDwDDcwYkaVdJ/wOsX0ytrNy+RmrdndvREfESsA/wFuATwDfzhgTA1cBS\nSVuQ2qptApRhyt8+xfH6IPAXYAvgf7NGlPyA1A7+7cXyH4Ev5ghE0kaSdgRWl7S9pB2K2x7AGjli\n6sAVpM/X4cCppN/lPTkCiYjbu7rliKkDX42IlyWNBd4PTAPOr+cOV6nnk1v5FZnXMcD7csfSH0ka\nAJwNfCpzKCuKVYAtgT1Io1p+K2mbiPhP1qj6p/HADyLiLEm7Aj+UNCoiXs8dmFl/EBGXAJdI2gr4\nNPCQpNnAxRHRkjG0NSJicsb9d2ZxRLxYM7Az9zzWQcAQ0t+WtarWvwQckiWi9ioH6wDghxHxSElG\nxr4eEUskfQQ4JyLOkXR/7qBYdt7yAeCqDl5vuawXET+VdDKki5iSlmaKZV/Sd9KNSd9PK14Cvpwj\noA68NSKmSZpYnITfLilLgkDSw3T8OSUgImLbBofUkcpr6QPARRFxg6TT67nDMiUIniFlKCs2LtZ1\ntM08SauQMuj/bEx4/UpPjiWS3g9MAd7nOUmd6u5YrgWMAmYWf6Q2Aq6T9CHXIVhOT16X80i1MRYD\nT0r6IylhkOUPR4n15Fg2AfsBRMTvJa0GrAd42kbv9egz1VY8Rb2Pdxe3fwAPApMkfS4iDs8U1vWS\nDoiIGzPtvzOPSDoCGFjUkzkB+F3OgKpOPn4QEU9BW2J/SHElOrc5km4hXUk9uRhGXIYk7mJJ44Gj\ngAOLdatmjKfiekl/ABYCx0lan3JM7Zwv6a0UJ5qS3kMa5dxwEXEZcJmkgyPi6hwx9MDi4t9nJX2A\nNAVyaKZYPphpv73xjKQLSXWnphbTf+o6C6BMUwzuAbaUNFzSIOBwoHbu2nWkDytImd/boixVFsul\n22MpaXvgQuBDnufdpS6PZUS8GBHrRcSwogDcnaRj6uTA8nryHr+WNHoASeuRphw80cgg+4meHMu/\nAnsBSBoBrAb8vaFRrjiuAz5ZzBN+D/BiRDybOyirL0nfBh4jXd39RkTsGBFTI+JAYPuMoU0knSgt\nlPSSpJclleFkdwKwNfAqMIN0xTLLMOsOnFHMXV8TmAs8KqkMQ9ObgJOAnYq6UINIo1Vy+zSwK9Ac\nEU9KGg78MHNMRMRJwG7AmOJCwnw6rgvVaJNIfyc2L0YZXU7+aX2zS1gXoeL0YsrW/wBfAi4B/jtH\nIBHxVOVWrNqyuP8CXdcmaqSPkaaw7FuMqB1KnafWlKaLAbRViv4OaV7Y9IholnQacG9EXFdcAfsh\n6Q/zv4DDI8InDx3owbH8DbANUPmS+9eVtVJnd7o7ljXbzgS+5ARBx3rwuhSpKMx+pCFVzRHx43wR\nl1cPjuVI4GLS8NoAToyIW/JFXF6SZpASU+sBzwOnUFwti4gLitfluaTX5QLg036Pr/gkfRr4aUTM\n7+CxdSIiyxVC6z1JD0TEdpKOBHYgnZTPyTV8WNK7I+IP6qTQWETc1+iYykzSnhFxm6SPdvR4RPy8\n0THVKkY2b0Uamv5YkcDIGc9NwKXAlIgYXcR3f0RskzOuspL0WeAYYGhEbF6MgrogIvbKHBoAkkYD\n7y0W74iIB+u6vzIlCMzMzMzKoJOTtxeBp4pCydlIegtp+tVqlXVFN44csZS+TZikR4DtSIX2zo2I\n2yU9GBGjM8VzUUQcI6mjWhYREXs2PChA0k8j4mMdzMvOOh9b0qkRcYqkSzt4OCLi6IYHVaWTxMWL\nwMO5RulKuicidpJ0f0RsX6x7ICK2yxFPTWzDSSMshlE13T3nZ4WkB0hFVu+qOl4PlyGhImkiqctX\nJRH2EVItgrp1YilTDQIzMzOzsvg+6WrzQ6QTpFHAI8A6ko7LNSJH0mdI0ww2JrUVfA+pfVmWk0pS\nmzCAj5Lq8PyoWB5PGpFTBheSKqU/SCp+uxlpCkQWEXFM8e+4XDF0YmLxb6nmZUfEKcW/ZZh+0ZEm\n0pSMSsJnD2AOMFzSaRGRY3pGaeoidOBaUiX+X1KOmhsAr0bEa0U9scqIkLJcRW8CdqmMZpM0lfSZ\nX7cEQZlqEJiZmZmVxd+A7SNiTETsSJre+ASpUNSZGeOaCOxEGskwrogrW6eXqnZgu0fEYRHxy+J2\nBMuGxGYVEd+LiHdExAGRPAVkPzlXx/3Ns9W3qKqt8g/g6eI4DQZGk94PWUmaWNSSkKRLJN0naZ/c\ncZEuuI6IiIMj4mBgJOnkchcgV8eRMtZFqFhUvCdbojwtBW+X9GVSe8i9gatICYwyEMs6GVDcr2v7\nDo8gMDMzM1veuyLikcpCRDxazB1/Qnlbqy2KiEWSkDS4mMu+Vc6ACmtKemelNlQxjHjNzDG1UaqW\nvjVV0zKA0zKFU/HViLhKy/qbfwu4gHRimdNvgfcWU1luIRXGPQw4MmtUcHREfFfSvsBbgU+QapPl\nrq+zSURUj5Z5oVj3L0lZahFExH2S3keJ6iJU+a6kU0i/t7Yuaplrb5xEulL/MPA54EZS8cQyuBS4\nS9I1xfJBpBEYdeMEgZmZmdnyHpV0PlAplHpYsW4wy9p05TBP0rqkYbq/lvRv4KlufqYR/pvU8vcJ\n0gnJZqQv2tlJugBYgzRq4BJSJ6y7swaVNLy/eQ8pIhYUVe+/HxFnFnO0c6tk5g4ALo+IR5Q5W1eY\nKel60lVngIOLdWuSaXSPpEOBm4tj9BVgB0mnl6QA5jak5M6eLJtiEOSbJgWwOqng88XQ1uJ2dVJh\n4qwi4mxJtwO7F6s+HRH313OfLlJoZmZmVkPS6sDxwNhi1WxSXYJFwBoR8Uqu2CqKK4TrkE4EXitB\nPIOBdxeLf4iIV7vavlEkPRQR21b9OwS4KSKyToEoTiqfIU1b2QFYCNydq3hiVVz3k1773waaipPM\n7AXbiiKF7wCGk6Y9DARmFlOAcsYlUlKgcgI3G7g6Mp5kVb3WxwJfJ9UK+X8RkXt0CpIeB0aW4TOr\nQtKdwPsrn+vFZ8QtEbFb3siSImGxIe2LOv61XvtzDQJbIUlqKYagVa/7YnE1qLOfmSlpTJ3jmiHp\nIUn/XbP+oKItXcNiqSdJgyR9R9Ljxe16SZsWj60m6W5JD0p6RNKpb+D515F0efHcf5Z0RTEUsvL4\nzZL+U3z5MjPrleLL2I0RcVZEfKS4/V9ELIiI13MkByStXfw7tHIjDYedRWpnWgZbkoY0jwYOk/TJ\nzPFULCr+XSDp7aQRIG/LGE9Fw/ub99BE4GTgmiI58E6WFeDLqYk0FHyniFgADAKyFy4s6lr8LCL+\nu7j9LGdyoFA9OuXiiLiBdLzKYC6wbu4gaqxW/ble3F8jYzxtJE0gFXz9NXA9cEPxb914ioGtqGYA\nh5P+8FYcDpyYJxyQtBHpj9oWHTx8EOnN/mgf71OkkUKNrhL7DWAtYKuIWKrUT/wXknYkzTfbMyJe\nkbQqMEvSTRFxZy+efxowNyI+CakFEvAD4MPF498ifbCXYnirmfUvxefW65LWiYiyVP6+klRdfg5p\nOG710OoA3pkjqIpiTvEepAJtNwL7k5IXl2cMq+KXxbSMbwH3kY7XxXlDguIk9+eSNqgk0YE/5IwJ\n2lpm/rZq+QnghHwRtcXxuqQngXdJWq3bH2gQpQ4B5wAjSCfhA4H5EbF2xrCekXQhaXTK1GJ0T1ku\nDK8L/EHSPbSvQZCzJep8STtUpmAU31cXZoyn2kTS9+l/NmqHnmJgK6TiysofgI2LtiXDSH/sNiMN\nEd2JNLfoZ5X2OZJmAl+KiHslvRIRQ4r1hwAfjIhPSVqfVECo8of8ixExu2bfqwHnA2OAJcCkiGiR\n9BDp6spjwISINh/JAAAgAElEQVSIuKPYfjdScuDF4nYw6QT4LtJ8yXVJQ/zuKK5qfZP0JWwwcF5E\nXFiz/2GkxMhdwI6kuXondfJ//gtwGXAgsCpwaFHwan3Sl9G3k1qp7A3sGBH/kPRx0heFQcU+jo+I\npVX7XwN4GhgeES9Vrb8D+HpUtQYrtp0FHBcRd9EDkrYgZVG3qOy3OC5/Jl2FeaxYtwfp91mqdk1m\n1j9I+gWpQ8CvgfmV9RGR/USpjCQ9TBo5cH9EjJa0IfCjiNg7c1wDgPdExO+K5cGkq4XZEz+SPgSc\nRfpb+wLpu8UfImLrzHGtT7qg0q6oY0TknCPeaYvPEsR1L+ki1FWk736fJBU5PTljTGsA+wEPR8Sf\nJL0N2CYytWetVkyNWk7OTgaSdiLVm/kbKfm6EXBYRMzJFVOFpBZg74hY0qh9egSBrZCKyrF3k65g\n/IL0wf3TiAhJU4rHBwK3Sto2Ih7q4VN/F/h2RMwqsv2/ImWMq30+hRDbSHo3cIukdwEfAq6PiO1q\nYv2dpOuKx34GkC78s0pE7CzpAOAUUoXjJuDFiNip+JIzW9ItEfFkTQxbAkdVrsp383/+R0TsIOl4\n4EvAZ4r93RYRZ0jar9gvkkaQCnXtHhGLJX2fVNW4+grRFsBfq5MDhXtJV5ZuKeKYU2x7Xk+TA4WR\nwAPVSYniat/9pN/FY714LjOzzvy8uJWCpB26erwExccWFld4lxTTIV4ANskcU+Wq83mkZA9FXYRS\n1EYgzQ1/D/CbiNhe0jjg45ljArgC+AlpxMqxwFHA37NGlFRafN4ZEeOK71jfyBwTABHxuKSBxXeT\nS4vvJNkSBGUdnQIpEVAkEHcqVt0dES9kjume4vVU6QhTpq4PT5CKXt5A+xEXZ9drh04Q2IqsMs2g\nkiBoKtZ/TNIxpNf/20gnnD1NELwfGKllRXPXljSkZj7qWNJQM4qr8U8B7wJqT5i7U/liOgcYVtzf\nB9i2GNUAqTjVlkBtguCpmiH7Xf2fq/fz0ar/w0eK/8PNSlWyAfYijUq4pzgGq5O+BPZK8Qd0u2LI\n5zWSRkXE3N4+j5lZvUTEZUqFCjetjEzK7Kzi39VIVykfJF3p2paUgN01U1wV9xaf6ReT/p68QhqB\nVga3SjoY+HkJ5oZXWxwR/5Q0QNKAYrThd3IHBbw1IqZJmlhc1b29GA6eW1lbfC6QNAh4QNKZwLNk\nHs7f2egU0qiQrCR9jDTdZybpM+wcSf9buUiWS5EQKON30b8Wt0E0qI6EEwS2IvsF8O3iqssaETFH\nqS/zl0i1AP4t6Qe074lcUf0FovrxylDFRdRfJUu4lGXvVZGmJ/yq4x9p0zYctgf/54720xkBl3Uz\nbO7PwKaS1oqIl6vW7whcXb1hRPynGDq1H1UfypI2AX5ZLF4QERdU/dijpOTCgEpthWII6WjS3FIz\nszdN0oGkyt+DgOGStgNOyzVPNiLGFXH9HNghIh4ulkcBX8sRU7WIOL64e4Gkm4G1ezE6r94+B0wC\nlkhaRPpbFpnniAP8R6la+m+BKyS9QNXf74wqV06flfQB0rDroRnjqShri89PkL4ffoHU7nMTll1w\nyaWso1MAppC+k74AbVNafgNkTRCUVUR0Wcxb0jkRMaEv91mWYhVmfa64qt8CTCeNJgBYm/TH98Vi\neNP+nfz485JGFCeeH6lafwvQ9iYsvjDWuoM07J5iasGmdD/s/WVSUb/u/Ao4Tqm4H5LepdRntys9\n/T9Xm02qroykfYBKh4BbgUMkbVA8NlTSZtU/GBHzSXUNzi6mEqBUyXoRaUrE+sUf+Eobsb2pGfYW\nEU9HxHbF7YKaxx4H7ge+UrX6K8CtUceWL2a20vkasDNFH/OIeIDMhQALW1WSAwDF6KvaqW5ZSHqH\nUl2dTYF1Jf1X7pgAImKtiBgQEYMiYu1iOXdyAFJh3YWkk8qbSQn2A7NGlJwuaR3gf0gXGC4hxZhV\npG4i/4mIrwFfJdVrOihvVAAcFBGLIuKliDg1IiaRpmfktDhSUbu20SmkkUdlMKBmSsE/8Tnpm7F7\n95v0jkcQ2IpuBnANaYoBEfFgMS/sD6RCerM7+bmTSIUD/04aullpIXUCcJ5SwcFVSFn/Y2t+9vvA\n+UXBpiXApyLi1appCR35MXCxpBOAQ7rY7hLSdIP7lJ7w73Tzx7EX/+dqpwIzJH2CNET0OeDlSEUK\nv0KqIzCAdJXh8yyfwT+ZNHzssSIJ8Hdg16IGxNuAy4rkwQBSbYjetms5mjQk7c+kBMg9VH2pUiqI\n+G5giKR5pCKP3Y26MDOrtjgiXqz57G50R5iOPCTpEuBHxfKR9HyaXN1ImkqqUfMoy1qsBVXV8HOR\ndGtE7NXdukYrEuoVl2ULpEbV3+QXScWSS0PL+sFXplZuRBp+ndNRpBpV1T7VwbpGKuvoFICbJf2K\nZRfvDiN1Psmm+E59JPDOiDitqNuwUUTcnTOuXNzFwMyWo1QAcWlELJG0K3B+bXHFXjzXRsBNxXNc\n1JdxFs+/Fakn7AkRkfUPjJmtOCRNI42aOonUXeYEYNWIqE0KNzqu1YDjgMrV+d+SPl8bMfWtU5Ie\nA7YtigCWQnGs1iCNJtyDZa0h1wZujoh3ZwoNAEkfBaYCG5BiK8XUh2LI92dJFyTaLiZGxNG5YoK2\nfvCnkHrCV5J1ERHbZopnPHAEqW7THVUPrU36DpUtAVWMLl1IuhBzJKlm1Y8i4l+5YqpW1ASpXPm+\nIyKuyRzP+aTX1J4RMULSW4BbImKnbn40O0n3RUSXRWx7/ZxOEJhZLUlbAj8l/WF5jdTKsAwFiszM\nGkKpTdgUUnFYkaZ4fT33iTi0Tc8qS/FEACTdRGqV+0q3GzeIpInAF0mF2p5hWYLgJeDiiDg3V2wA\nkh4HDoyI1pxx1JL0O9IJ7xyWjQYhIq7u9IcaoDheu0QD+8F3pZhiORw4g5RIrHgZeCga2JaulqSp\nETG5u3WWVE6yJd0fEdsX6x6MiNG5Y+tOdcx99pxOEJiZmZn1D0V18m8BgyIie/FESeeQphK8g1Qs\n9lbat+I6IUdc1SRNiIhzcsdRS9LsiOjz+cNvlqQH3uiowXpShn7wPVG5Wh+ppea7SFMcb8rZJq+j\nq8qSHso12qImjtKNnJF0F7AbcE+RKFifNIKgT0+83whJ21TXneng8U9FxA/6dJ9OEJiZmZm1V3zR\n/xLLD7PeM1dMAJLmAHsCM6uudD0cEdtkiueorh6PiFLMrS+KJw6j/e/y8kyxVCrcv480h/5a2idV\nft7RzzWKpNOB35Vl2p6kScXdrUl96hvWD74nivfke0kFnWeT6iK9FhFHZojlOOB4UkHVP1c9tBYw\nOyKydzIo48gZSUeSaiHsQKoHcgjwlYi4KmtgtNXVGgz8ALgiIl6s9z5dpNDMzMxseVcBF5CKwy7t\nZttG6qh4YrarPbUJgKLLzijgmZpK5dlI+iGwOfAA7QsoZkkQ0L5TwQLSNJaKALIkCCS9XOxfwJcl\nvcayloc5r/BWujw1vB98DykiFkhqAr4fEWdKeiBTLFeS6j4tN+2hLPUHgOfLlBwAiIgrikTPXqTX\n/0FliTEi3ltM/T0amCPpbuDSiPh1vfbpEQRmZmZmNSTNiYgdc8dRq2zFEyVdAJwTEY8UrfF+TzoJ\nHwp8KSJmdPkEDSCpFRgZ/tK7QpC0Nilh8XLuWCDNASddtf82qWvSIzlH9VQr2lKvVlkuQztoSd+l\nZCNnJA3tYPXLOaeJ1Co6eBwEfI9UR0XAl+tx3Nxz0szMzGx5v5R0vKS3SRpaueUOCphAGmr9KqlN\n2EukQny5vDciHinufxr4Y3FitCNwYr6w2plLOiEpFUnvlPRLSX+X9IKkX0ganjsuSNMgJJ0t6SxJ\nXbZTbhRJY4oW0g8BD0t6UFIZknhfJLV3vqZIDryT1DkjG0kHSvoTqR3k7cBfSCMLymBtlo2cObC4\nfTBrRHAfqSX3H4E/Fff/Ium+3K8xSdtK+jbQSppedmBEjCjuf7su+3Qy1czMzKw9SU92sDoi4p0N\nD6bEaqp+3wBcVSmYVY/q2m9EUdxuO+Bu2l+xzFLYsULSncB5LOsHfzgwISJ2yRcVSPo+sAXt+9T/\nOSI+ny+qVGQP+HxE3FEsjyUN6c9eeK9sJD1IOoH8TURsL2kc8PGIaMocWilJuhj4WUT8qljehzRC\n61Lguznfk5JuB6aRPlsX1jz2iYj4YZ/v0wkCMzMzs3KTdF1Xj2fsYtACnAX8DbgNeHdEPCdpFWBu\nRLw7R1zVJL2vo/URcXujY6nWUVX5MrRWk/QHYERlSoakAcAjxVXLnHEtl3CqRw/4XsRTyvckgKR7\nI2JMkSjYvuiwkPW1JenEoj5DpfNJOzk7nnQ0JaTy/ixrV496cpFCMzMzs0LlS2xx/9DqKtaSvhER\nX84U2q7A06SruneR5p+WwedIc2I3Ar4YEc8V6/ciVZvPLiJul7QhsFOx6u6SFFC8SdJJwI9JJ0yH\nATdWprJkLCr3OLAp8FSxvEmxLrfbJV1Ieg9UjtdMSTsARMR9DY6nrO9JgP9IGgLcAVwh6QVgfuaY\nKkX/7s0aRceelTSZ9F6E9Np6vpj3/3q+sFLyguUTKi+SjuPpEfHPPt+nRxCYmZmZJdVXJGuvTma+\nWjkQ2BsYD2xLOvmeUTX/P5sitom52811RtLHgG8BM0knce8F/jcifpY5ro6msVRkm85SDGneiTQl\ng+L+vaSTktyjVToTjW5BWvL35JrAQlK9uSOBdUgt8vr8ZHJFIGk94BRgLOlkfDZwGuk1v2lEZEuQ\nSTqTVPj1ymLV4cAawHPA2Ig4sLOffcP7dILAzMzMLKmZU99uSHOJ5tQPJp2UfAs4NSLOzRwSku6O\niJ1zx9GRYpj13pVRA5LWJ83NzjqUv6w6m5JRkXtqRhmV7T0paV1gy2LxjxHxYs54qkn6NXBoRPyn\nWH4L8OOI2DdTPAOByyPiyBz7705HienKunp1y/AUAzMzM7NlopP7HS03VHES8gHSicgw0tD+a3LG\nVGW2pHOBn1A1lDnDsO+ODKiZUvBPStDJqzgx+QDpd9n2nTz3SIxKAqBoJ1gdV64pD0DbSe8nWf54\n5Zy7Xqr3ZBHPhaR2eE+SRsxsJuka4NiIeC1XbFXWryQHACLi30U7xiwiYqmkzSQNKsnxqTVQ0s4R\ncTeApJ2AgcVjS+qxQycIzMzMzJYZLanSY3r14j7F8mqd/1h9SbocGAXcSLpCOTdXLJ2oFPE6rWpd\nkCqp53azpF/Rvip/GVq+/RJYBDxM5nnO1SQdQ/o9LiLFJdLvMncHjxuBOynJ8Srpe3IKsCqwSUS8\nDCBpLVK3jK8Wt9yWSto0Iv4KIGkzMidfgSdISc7raJ/gLMO0qc8A04uaEiK1tm0qppGcUY8deoqB\nmZmZWS9JektE/LuB+3udZV9cq7+8iTT/eu1GxdIfSfooaX4xwB0RkX3kRUddDMpA0p+AXSPiH7lj\nqZazBkhHyvielDQX2DkiFtSsHwLcGRGjGh1TLUn7AhcDt7OsJsgxlRaDmWI6paP1EXFqo2PpjKR1\nABoxXcQJAjMzM7NeKtvJSkWjExdV+90Q+Abw9ojYX9JI0knmtEbHUhXTFsCGETG7Zv1Y4NmI+HOe\nyNrimArcGhG35IyjlqSbgY/WnmTmJum/gVeA64FXK+tzT30ok66STvWar94bRcvMQ0gtUd9TrL6z\nbMmoMikSA6cA/1Wsuh04rZ6JAk8xMDMzM+u9MrU0q3YrkCNx8QPgUtIQZ4A/kuoRZEsQAN8BTu5g\n/YvFY31e/buX7gSuKU6aFlOe0SAnA7+TdBftT8SzzfUvvEYqAjiFZVfsyzD1oUyiKPrX0edT9mkZ\nEfF60Ur2p6RETykUhUtPBLamaipZoztjdGI6MBf4WLH8CdJn7UfrtUMnCMzMzMx6r6xDMHMlLtaL\niJ9KOhkgIpZIWpoplooNI+Lh2pUR8bCkYY0PZzlnA7sCD0e5hvReSLrCW4q5/lX+B9jCV5u7tA4w\nh44/B8ryGvuNpC+xfEHTnCNBriDF80HgWOAo4O8Z46m2eUQcXLV8qqQH6rlDJwjMzMzMVhy5TgLm\nS3prZf+S3kO6Up/Tul08tnrDoujc08DckiUHAFaNiEm5g+jA40Cppj2UTUQMyx1DDxxW/Pv5qnW5\nR4K8NSKmSZpYdPG4XdI9GeOptlDS2IiYBSBpd2BhPXfoBIGZmZlZ75V1ikEuk4DrgM0lzQbWBw7N\nGxL3SvpsRFxcvVLSZ0hXWXN7Apgp6SbaD+XPXTn9pqKTwS8p11z/+cADkloo19SH0pEk4EhgeER8\nXdKmwEaVVnk5RcTw3DF0YHHx77OSPgD8DRiaMZ5qxwGXFbUIBPwL+FQ9d+gihWZmZmZViv70j0TE\nu7vYZmgJTpiWI+n+iNg+w34HA0uBrUhfYh8DBkTEq13+YH1j2pDUk/41liUExgCDgI9ExHO5YoPy\nVk6X9GQHqyMiss71l3RUR+sj4rJGx1J2ks4nTQ/ZMyJGFHUJbomInTKHBoCkUcBI2s/3vzxjPB8E\n7gA2Ac4B1ia1rrwuV0y1JK0NEBEvdbftm96XEwRmZmZm7Un6BTCh0qu7DMqcuOioq0NZOj1IGkfq\nVw/p+N1W83iWzg8dkbRKRCzJHUeZSFq7s5MiSZuW6T1aFpX3XnXCUNKDETG6BLGdAuxBShDcCOwP\nzIqIQ3LGVTaSupzmU8+RRp5iYGZmZra8twCPSLqb9oW0PpQroIhYKumxrk6KGp0ckLQR8A5gdUnb\ns2zqxdrAGo2MpTMR0QK0dLFJQzs/SJoVEWOL+z+MiE9UPXx3I2OpievEiDizuH9oRFxV9dg3IuLL\nOeICZlIcE0m3RsReVY9dS6bjVXKLi4RipSbI+pSn4OQhwGjg/oj4dDHS50c5ApF0Dl3Ubck8fWWt\nXDt2gsDMzMxseV/NHUAnypa42Jc0H3Zj4CyWJQheBnKdUPZWo+tJrFl1f1TNYzlrWxwOnFncPxm4\nquqx/cj3+6w+JrXzwl0LpGPfI02v2UBSM+mk/Ct5Q2qzsGh3uKQYNv8CaWh/Dvdm2m+3ck41coLA\nzMzMrEZE3C5pM2DLiPiNpDWAgbnjomSJi2L+92WSDo6Iq3PH8wY1er5tdHK/o+VGUif3O1pupLIe\nr9KKiCskzQH2Iv3uDoqI1sxhVdwraV3gYlJtkFeA3+cIpKf1KySdExET6h1PJ/vemFQXYfdi1R3A\nxIiYV699OkFgZmZmVkPSZ4FjSFcsNycNo7+A9IU7mxInLjYurga+TPrivwNwUkTckjesUlpX0keA\nAcX9jxbrRepjn0tZT8Q3KOZjq+o+xfL6+cIqr6LN6CMRcV6xvLakXSLirsyhERHHF3cvkHQzsHZE\nPJQzph7YvftN6uZS4EqWdYX5eLFu73rt0EUKzczMzGpIegDYGbirqsjXwxGxTea42hIXEbG5pC2B\nC2rmZeeI68GIGC1pX+BY0nDmH5ahSGF3Gt35QdKlXT0eEZ9uVCzVJC0lTVsRsDqwoPIQsFpErJop\nrg67PVTk7vpQRpLuB3aI4kRP0gDg3jK8H4vk2G0R8WKxvC6wR0RcmzeyzuUsuCrpgYjYrrt1fckj\nCMzMzMyW92pEvJbaiafq8pRjOPPnKRIXABHxJ0kb5A0JWDYE/QDg8oh4RJWDVwJFm7dNqPruGxH3\nFXcbmlzJlQDoTkSUYSTKcpwAeEMUVVeBizn/ZTnvOyUirqksRMR/iiRQaRMEmf1T0seBGcXyeOCf\n9dxhWV4oZmZmZmVyu6Qvk6rz7w0cD/wyc0xQ3sTFHEm3AMOBkyWtRUmqpkv6OqmQ4p9ZdqwC2BMa\n3/mhKq7BwMHAMNonLk7LFE9tAcB2Mh6n73X1eOZK82X1hKQTgPOL5eOBJzLGU21AB+vKfk6aM9l5\nNKkGwbdJn1u/I32e1U3ZfxlmZmZmOZwENAEPA58j9eu+JGtESVkTF03AdsATEbFA0luBslwp/xiw\neUS8ljuQGr8AXiQVans1cyyQ4gg6PhkK4J2NDafNnEz77c+OJXUy+Arpd3craWpSGdwr6WzgvGL5\n82T+HUvaJiIe7mKT7zYsmOVtXNulRtLuwNP12qFrEJiZmZn1E8Vc4iZgH9KJ3K+ASyLzFzpJ/9XR\n+oj4baNjqSXpauC4iHghdyzVJM2NiNo2h9YNSWtExILut1w5SRpImuZzZO5YOiJpTVI3lvcXq34N\nnB4R8zv/qbrHdAcwGPgBcEWlPkIZdFT/oN41EZwgMDMzMytIepguhuxHxLYNDKffkFQ9imE1Up2E\nORGxZ6aQ2kgaQ7paP5eqK/W1V+UaTdJFwDndXLlsuKJ2xJHA8Ij4uqRNgY0i4u7Mce0KTAOGRMSm\nkkYDn6uqim8FSbOAPUs4aqZNMQ0pIuKV3LEAFAVfjyZ1C7gbuDQifp0xnl2B3YAvkqYXVKwNfCQi\nRtdr355iYGZmZrbMB4t/P1/8+8Pi34+Tca5/2RMXEXFg9bKkTYDvZAqn1mXAVNJ0kVLURSiMBT4l\n6UlS4kKkE6bcSajvk47TnsDXSa0rrwZ2yhkU6fW0L3AdQEQ82NnIFeMJYLak60idKQCIiLPzhZRI\n2ga4nNRCFkn/AI6KiLk54yoKvn4FuJc0PWP7Iln25Yj4eYaQBgFDSOfra1Wtfwk4pJ47doLAzMzM\nrBARTwFI2rum9d1kSfeRahPkUMrERRfmASNyB1FYEBFdFrrLZP/cAXRil4jYoWiVR0T8W9Kg3EEB\nRMTTNc0xluaKpeT+XNwG0P7ksgwuBCZFRAuApD2Ai0hXy7OQtC2pZsoHSFMeDoyI+yS9Hfg90PAE\nQUTcTqo584Oqv0sDSCNoXqrnvp0gMDMzM1ueJO0eEbOLhd3ouPp2Q5Q4cQGApHNYlqgYQCpYeF/n\nP9FQd0g6g3TluXqKQe74ypjYAVhczGMPAEnrU46RF08X78OQtCowEWjNHFMplbw15JqV5ABARMws\n6hLkdA5p+sqXI2JhZWVE/K0YVZDTGZKOJSXD7gHWlvTdiPhWvXboBIGZmZnZ8o4GLpW0TrH8n2Jd\nbqVKXFS5t+r+EmBGJcYSqCRU3lO1rq3NYUY3sKxrwGqkFpGPAVvnDIo0vPoaYANJzaThzLlPkiBV\n5v8u8A7gGeAWlo2osSqSWuggAVWGmiCkFoxfpf0oqKwtGCPifV089sPOHmuQkRHxkqQjgZtIyeA5\ngBMEZmZmZo1QXD19X0SMriQISlTVupSJi4i4LHcMnYmIcblj6EhEbFO9LGkHUtvKrCLiCklzgL1I\nyYuDIqIMV+pV1sr8JfSlqvurAQeTEndlcDRwKsuG7d9B5s+wTmq8vEhKfJ4eEf9sfFRtVi1GzBwE\nnBsRiyXVdfSRuxiYmZmZ1ZB0d0TsnDuOakXi4oSI+HZZEhddFE8sS8E9JH0DODMi/lMsvwX4n4go\nw1XxdiQ9XJs4yBDD94AfR8TvcsZRS9Ifgb8APwGurvw+rWfK+JlWFpLOJA3hv7JYdTiwBvAcMLa2\nCGuDYzsBmAw8SKqRsCnwo4h4b9326QSBmZmZWXuSvg2sSjoZqa4CnnXeetm+5EvarKvHK7UTcpJ0\nf03dhrr3Ee8JSZOqFgcAOwJDI2LfTCEBIOko4DBgK9JUgx9HxL1d/1RjSNqZdPJ2EPAoKbYf5Y2q\nfCQNrVqsvLa+FxFbZQqpjaR3kUY4DKNqNHvO6Q8dfR5U1pUhaVdL0ioRUbcRIZ5iYGZmZra87Yp/\nT6taV4Z567MlnUt5EherAhvW1huQtDvp6lsZDJQ0OCJeBZC0OjA4c0zQvrr8EuB64GeZYmlTTBe5\nrDjJPBiYKmnTiNgyc2hExN3A3cWokLNJLSydIFjeHJbVt1gCPAk0ZY1omauAC4BLKE8XioGSdi5e\nX0jaCRhYPJZlaoakj0fEj2oSidXq1rLSCQIzMzOzGmWdt075EhffAU7uYP1LxWPZhuZWuQK4VdKl\nxfKnSSeWWdVWmi+urJ4LfDZPRMvZAng3sBkl6BYgaW3gI6QRBJuTRjeUZjRNmUTE8NwxdGFJRJyf\nO4ganwGmSxpCSqq8BDQV3RXOyBRTpbNDw9tUeoqBmZmZWY1ijv8pwH8Vq24HTss9579sJN0TETt1\n8lhphuZK2p9UdA/g1xHxq4yxbAv8H/B24FrgPFJiYBfgrIj4dq7YoG0+9keAPwM/Bq4tw3x/SU+S\njtdPI+L3ueMps6Ko3XEs+/yaCVwYEYuzBVWQ9DXgBVKCp7rt6L9yxVRRltouuTlBYGZmZlZD0tXA\nXJZdaf4EMDoiPpovqvIlLiT9qbOh55Iej4gtGh1T2Um6Czgf+D2wP2kExmXA/4uIRTljA5D0OVIR\nwH/kjqWaJEVESFojIhbkjqfMJF1Cmv5T/fm1NCI+ky+qpEj01IqIeGfDgymU7XO1iOl7XT0eESfU\nbd9OEJiZmZm1J+mBiNiuu3WNVrbEhaQZwG0RcXHN+s8Ae0fEYTniKmJ4ma47LKzd4JDSzmteR5Ke\nyHlyVEvSAOAI4J0RcZqkTYGNKvOzM8a1KzANGBIRm0oaDXwuIrK3hiwbSQ9GxOju1llSts/VIqaj\nqhZPJSUw2tSztaxrEJiZmZktb6GksRExC9qK7i3MHBPA5hFxcNXyqZIeyBYNfBG4RtKRpMJoAGOA\nQaRh6tlERMPn7vbQapK2JyUqAF6tXs7dKYM05eF1Ul2L04CXgauBDqeSNNB3gH2B6wAi4kFJ/9X1\nj6y0lkraPCL+DCDpnZSkIKCkNYBJwKYRcYykLYGtIuL6jGGV7XO1XQJA0hfrmRCo5QSBmZmZ2fKO\nBS6vzEkF/g0c1cX2jVKqxEVEPA/sJmkcMKpYfUNE3Fa9naS3RMS/Gx4gy7V8q3g543zs52hfgbx6\nuQydMnYp2rvdDxAR/5Y0KHNMAETE05KqV5XipLeE/hdokfREsTyMVJyzDC4lJRN3K5afIXU2yJkg\nKNXnamW0PvAAACAASURBVAcaOuTfCQIzMzOz5b0UEaOLyulExEuSylAZvJSJi4hoAVq62ORWYIcu\nHq+n+4BNSMdKwLrAc5KeBz4bEXO6+uG+FhF7NHJ/b8BiSQMpTkokrU8aUZDb05J2A6IowjeREnRX\nKJOiPd/TEXFrcWX+c8BBwC3Ag1mDW2bziDhM0niAiFigmqxPBseRWnuuQ/qM+BfwqawRZeQEgZmZ\nmdnyrgZ2iIiXqtb9DNgxUzwVZU1cdCfnCcCvgZ9VOhdI2gc4mHQl8/uk7gENI6nLec0R8fNGxdKJ\n75EqzG8gqRk4BPhK3pCAlBz7LvAO0lXnWwDXH2jvQuD9xf1dgJOACaT2qBeRfpe5vSZpdZYloDan\nqptBDhHxANDuc/X/t3fnUXeV9dnHv1cigiaEoQyCQBQFbDQMgVQiIBLrWKABB9oKr1LHdjFYKir6\n2qC0UhG1DH0dkAZEcAiCEH2roiABDNOTEQQVFWItQwWZ8mKEcL1/7H2Sk5PzZICw7/2cXJ+1sp7n\n3pvHc60krpX927/7/pXMA6ucofJcSZ1Mz/gZKikQRERERNQkvQR4KbBZz4PcOGCTMqlW0tbCxZqU\nPBV7X9vvXh7E/oGk022/V9LGBfIcspp7BooWCGxfKGmIaiykqN5AFx/7Vk9VeFv3NUmnAx8ok6iV\nRneNCzwC+JLtbwHfKr2nvst04HvAjpIuBPaj0Nt6SScMcx0A25/td78JJc9QSYEgIiIiYoXdgIOp\n2tC7H+QeAd7d9ycaMAIKF212t6QPAV+v10cA99Zt9I23zttuy17wYdm+Hbi9s5a0GNipXKJhvZUU\nCLqNlvQs209QFXje03WvFc99tq+QNBfYl6oAdXzBkZptPci0qFb8RYmIiIhoA9uXAZdJmmJ7Tuk8\nXVpZuFgHJbcY/A3VW8tvU72hv66+NprqAbMISdsCnwS2t/0GSROAKbbPLZVpNUrvER9OW3OV8jXg\nakm/ozpk7xoASS+mcBeIpN4zSO6uv+4kaacS0ztsf7zpzxwJZJfs+IqIiIhoH0mnAf9M9Y/s7wG7\nA/9g+6uFc7WtcLGcpC2oDgNc/gKq849+SVt2tT63iqSzbB9b4HP/k+ochI/W50o8C5hne2LTWdZE\n0mLbRToIhplCAVVxYIHtHZrM03aS9gW2A35ge0l9bVdgbMkRmpI6h5huQjUKdQHVn+HuwM22pxTM\ntgNwFtV2B6gKK8fb/q9SmUpKB0FERETEql5r+4OSDgPuBA4HZgNFCwTAYZJupX2Fi1Oo9hH/khXn\nDSwf2dfW4kBtvzX/J8+IrWx/U9JJALafkFRsbJ+ks+h/VkRn8kMpQ1S5+nUL/LHhLK1n+/o+135e\nIktPhoMAJF1CdY7Konr9MuDkgtGgKtRdBLylXh9ZX3tNsUQFpUAQERERsaqN6q9/Acy0/VD5SVxA\newsXb6UaX5YHtrW3RNKfsOI0930p2wZ+81O894yyPRKmdMTa261THACwfYukPy0ZCNja9oyu9XmS\n3l8sTWEpEERERESs6nJJt1O9qf+7ehb8HwpngvYWLm6hest8X+kgI8g/ApcDL5J0HbA1BcfQ2T4f\nQNLE7ge4tpD0LeBc4Hu2Gz9cMtabhZK+zIqi5tuAhQXzANwv6UiqMxwA/hq4v2CeonIGQUREREQX\nSaOoTti+HXjI9jJJY4BNbd9TONupwGFUhYs/o3oo/47tlxfOtQ9wGVWhYPlMc9uHFgu1liTNs71X\noc9+FtUBlAJ+ZvvxEjm6SboG2Bg4D7jQdvERhwCS/hw4mur/mzOBGbZ/VjZVrCtJmwB/B7yyvjQb\n+LztYgVYSeOpziCYQtXR8xPgWNu/KZWppBQIIiIiInqUfGgcTssLF7cCXwQW0TU60PbVxUKtJUnv\nsH1egc9dSDV68Ru2f9n056+OpF2Av6Xak30jcJ7tH5RNVZG0GdUb3o8CvwHOAb7ahuJKjEyS9rN9\n3ZqubShSIIiIiIjoIel0YA5wiVv0j6U2Fi4AJN1ke3LpHN0kzaL/oXtA+e6G+q3lEfWvJ4FvAN+0\nvbhkrg5Jo4FpwJnAw1RdDh+xfUnBTH9CdYDcUcB/AxcC+wMTbb+qVK5Ye3Xx6VRgAtVEAwBs71ww\n01zbk9Z0bUORAkFERERED0mPAGOAJ6jOHhBg2+MK52pr4eKzVFsLLmflLQYlx6oduLr7bepuqB+a\nPga8zfbowll2p2rl/wvgCuBc23MlbQ/MsT2+UK5LqbZjXEDV0XB3172bbe9TIlesG0nXAtOBzwGH\nUP1dG2X7nwpkmQK8Anh/nadjHHCY7T2aztQGKRBEREREjBAtLlxc1eeybU9tPMwI0tNFsIxqu8Fn\nCme6GvgycLHtx3ruHWX7gkK5DrLd7+9ZjCCShmzvLWmR7Ynd1wpkORB4FfA+4Atdtx4BZtn+RdOZ\n2iAFgoiIiIiapJfYvl1S39bSkm/E46lpY0szgKQbqKZSzKQqDPyqZJ6RQNLLWPXP8SvlEsW6kvQT\nqm0hFwNXAr8F/tX2bgUzjbd9V/39KGCs7YdL5SktBYKIiIiImqQv2X5P296It71wIemTwGm2H6zX\nWwD/aPt/l8xVZ2lNS3NPrt3aeAp/iwsq06ne9k4A/i/wBuBa28VGQ8a6kzQZuI1qAsspVO38n7Z9\nfcFMF1F1ESwDbqoznWH706UylZQCQURERERN0ltsz5S0c5ve6La1cNHR7/DEthzy1aaW5p5c2wKf\nBLa3/QZJE4Apts8tnKutBZVFwB7APNt71L9/X7X9mpK5Yv2SdJbtYxv+zPm295T0NmAS8GFgyPbu\nTeZoi1GlA0RERES0yEn114uLpljVFfXXd9o+qOdXG/b5j5a0cWch6TnAxqv575u0tG4b/oWkYyQd\nBowtHQo4D/g+sH29/jnVYWmlPcf2j6heJN5l+2SqAwtLe8z2k8ATksYB9wE7Fs4U699+BT5zI0kb\nUU3tuLwembnBvkV/VukAERERES1yv6QfAC+UdHnvzYKj8U6i2qt+MdUbrra5EPiRpBn1+mjg/IJ5\nuh0PPBc4jqqleSrw9qKJKlvZ/qakkwBsPyFpWelQ9BRUqPaIt6GgcrOkzYFzgCHgUaqJHhFP1xeB\nO4EFwOz68NCcQRARERGxoZP0bKoH8AuAd/XeLzUaT9IVVG+0JgPX9N4vWLhYTtIbgFfXyytsf79k\nnraT9GPgTVS/V5Mk7Qt8yvZqxzM2kKuNe8QF7GD7N/X6BcA42wtLZYpnRou2Jj3L9hOlc5SQAkFE\nRERED0lb2/6f1dxvdJ9sWwsXI4GkXYETgfF0dc+W3ppRHzh5FvAy4BZga+DNJR96JY2mKlJ8oFSG\n4XSfIRGDq995Js/gZx1p+6uSTuh33/Znm8jRNtliEBEREdFjdcWBWqP7ZG3/Ebhe0itaVrh4hP57\ndUV1eOK4prKsxkyqGefnUJ1S3gq259Zz2Hej+v36GXAoUKxAYHuZpP1Lff4azJU02fZNpYPEUydp\nou1Fq/lPzmgsDIypv27a4Ge2XjoIIiIiItZRW9pge7U1V0ltmFiwtiQttr1T4QyfB55PVVhZ0rlu\n+5JioQBJtwMvBu6iytUpQm2QJ82PVJKuoTrA9DzgQtsPlU0UvdJBEBERERFPi6Qt+1x+pD4NvLRZ\nkv4euBRY2rlo+4FykYal0gGATYD7qQ5z7DBQtEAAvK7w58d6YPsASbsAfwsMSboRmGH7ijX86Hon\n6czV3bd9XFNZ2iQdBBERERHrqMl9suuiVAeBpDupRs79nuohd3PgHuBe4N22h5rO1JXt130u2/bO\njYdZgzZ0ELSdpOcDo+vlf2+oB8mNdPV5F9OAM6kmBgj4SJOdKpK6p5l8HJjefd92WyaxNCoFgoiI\niIh1JOkdts8rnaNXqcKFpHOAizuTCyS9luqE/hnAGbZf3nSmtpK0iOHPbdjV9sYNR6o+XHop8CLb\nl9frzwGb1bfPtj23UK6TgI1sf6JeLwYeBJ4NnG/71BK54qmRtDvVGNS/AK4Azq3P49gemGN7fKFc\nrSz6lpACQURERERN0iz6P7wB7RgnuDqlChf9TpiXtND27pLm296zQKaptq+UdHi/+6X21Ncz1odl\n+66msnSr/+6favsn9fqnwMeA5wJvsj2tUK65wAG2l9Trebb3qt9AX227rYcqRh+SrgbOBWbafqzn\n3lG2LyiUK+e31HIGQURERMQKp5cO0M/aFi4KdjXcLelDwNfr9RHAvfVD3JOFMh0IXAkc0udesT31\nnQKApGOBC2w/WCJHH9t1igO1h21/C0DSewtlAqBTHKidUV9bJuk5hSLFU2T7wNXcK1IciJWlgyAi\nIiKi5epxeMOyfXVTWfqRtBXV/t39qR6+rwM+ATwE7GT7joLxWknSPwN/BcwF/gP4vgv+w1zSz2zv\nNsy9n9vetelMnc8GXtp74KWkjYFbbO9SIlc8NcNssXkIuBn4Z9v3N5ile0zrc4H/17lFe8a0Ni4F\ngoiIiIge9SnbpwITqE51B6CNB9uNBJLOsn1soc8+oc/lh4Ah2/ObztNNkoDXUu3J3gf4JtWe7F8W\nyHIV8GHbN/Rc3xf4V9uvajpT/fmfBJ4HHGP7/9XXxgBnA/fYPqlErnhqJJ0GLAMuqi/9FdXD+T3A\n/rb7dfxEg7LFICIiImJVM6jeiH8OOIjqAW5U0USM6MLFfgU/e5/616x6fTCwEHifpJm2TysVzLYl\n3UP1cPQEsAVwsaQrbH+w4TgfAr4h6TyqrgaAvYG3U20ZKeVjwL8AiyXdRfV2d0eqfewfK5grnpo/\n79nrv6iz/1/SkcVSxXLpIIiIiIjoIWnI9t7dh+91rhXOdS0rCheHUBcubP9TyVxrUvIAMEmzgTfa\nfrRejwW+C7yeqotgQqFcxwP/C/gd8GXg27YflzQK+IXtFxXItA1wDPDS+tKtwL/bvrfpLL3q8wZe\nXC/v6D3gLkYGSQuoRp/eWK8nA1+2vUcmCbRDOggiIiIiVrW086Am6Rjgt8DYwpkAnmP7R5JUH3Z3\nsqQhoNUFgsK2AZZ2rR8HtrX9mKSlw/xME7YEDu+dWmD7SUkHlwhk+776lPlTW/gAfgVwNXANcGfZ\nKPE0vAv4j7pQJ+Bh4J31tpGMrGyBFAgiIiIiVnU81b7Y44BTgKlUrdaltbVwsSYq+NkXAjdIuqxe\nHwJcVD+Q/LRUKNvTYflb++7tIott31YqF1VXw+clPUD1MD4buNb27wtmAjgKOAB4E/Dpurhzje1/\nKBsr1oXtm4CJkjar1w913f5mmVTRLVsMIiIiIkaIuh33NmBzqsLFZsBptq8vGmwNJL2j4AhGJO3D\ninMQrrN9c6ksHZIOAT4LbA/cB4wHbrP90tX+YEMkbQ+8GfgAsL3t4i8WJW1HNb7yAKqzQRbbfn3Z\nVLEu6sLAdOCV9aWrgU/0FAqioBQIIiIiInpI2hU4keqhbfmDke2pxUK1kKRZrDqybDnbhzYYZyWS\nxtl+WNKW/e7bfqDpTN3qvdhTgR/a3kvSQcCRtt9ZONeRVA/gE6nOR7iW6k39nMK5flnnuYiqs2G+\n7SdLZop1J+lbwC3A+fWlo4A9bB9eLlV0S4EgIiIiokf98PYFYIhqJBcAtoeKhaJ9hQtJB67uvu2r\nm8rSS9J3bB8s6desXMTozDgvOvlB0s2296n/ru1Vnz2wwPYehXP9Dvgl1d//q2zfWTJPR32o4/5U\nEwxup3rzPLvESMh46iTNt73nmq5FOSkQRERERPRow8SCftpauGgrSQJ2tL24dJZekn4ITKM6mG0r\nqm0Gk22/omgwQNJLqVrA9wd2AX5m+6iyqSr14XZHU2192MH26MKRYh1ImgOcaPvaer0fcLrtKWWT\nRUcKBBERERE9JJ1M9cB2KV0n4LegLb2thYtdqB50J7DygXtF39IDdI+qbJP6kMTHgFHA26jOk7jQ\n9v2Fc42jOq+hs9d/K+B620UP6ZT0GaqCxVhgDtU2g2ts/6pkrlg3kvak2l6wGVU3zwPAO2wvKBos\nlkuBICIiIqJH3Zbeqw1t6SfTzsLFtVQHj32OakrA0cAo28XHL0o6Hzi7Pj29FSSNpjp74KDSWXpJ\nWkh17sC1VC38/1U4EgCS3kxVELi3dJZ4+upCFLYfLp0lVpYCQURERMQI0eLCxZDtvbvf1rel20HS\n7VRt8ncCS1hxBsHuhXP9CDi8rae316382H60dJYOSYfSdfq97Vkl88Tak3TC6u7b/mxTWWL1io8r\niYiIiGgLSVNtXymp74nati9pOlPP57+w5OevxlJJo4BfSDoG+C1VK3gbvA7YgqpdHmA28GC5OMs9\nCiySdAVV4QIA28eViwSSXgZcAGxZLfU/wNtt31I416nAnwEX1peOkzTF9kcKxoq1t2npALF20kEQ\nERERUZP0cdvTJc3oc9u2/7bxULS/cCFpMnAbsDlwCtX+4tNsX18yFyw//f5dwCVU3QPTgHNsn1U4\nV989/bbP73e9KZJ+AnzU9lX1+lXAJ0sfnlhvfdizM9qw3qYxr3QnSMSgSYEgIiIiouXaWrgYCeoH\nyym2l9TrMcCcPFj212/UYkvGLy4EXtU5b0PSlsCP8+c4skjaATiL6iBMqA6bPL4tZ11EthhERERE\nrGKY/bIPAUO25zedx/b0+uvRTX/22pC0K3AiMJ6uf1/anlos1AqiayRk/b0KZUHSX1KN5/v3en0D\nsHV9+4O2Ly6VrfYrSR+j2mYAcCTQhkkBpwLzJF1F9ef3SuDDZSPFUzADuAh4S70+sr72mmKJYiXp\nIIiIiIjoIekiYB+gcwjawcBC4AXATNunFcrVqsJFh6QFwBeAIboexm0PlcrUUf+evZ1q8gNUWwzO\ns/1vhfJcB/yV7d/U6/nAq4ExwAzbry6RqyvfFsDHqUYKmuoN78m2i5/bIGk7YHK9vBEYb/uGgpFi\nHUmab3vPNV2LclIgiIiIiOghaTbwxs4J7vWJ7t8FXk/1MD6hUK62Fi5aMbFgOJImUT3wQjUqb17B\nLDfZnty1Ptv2MfX319vet1S24Uj6hu0jSufoJWmx7Z1K54i1V0/vmAF8rb7018DRpQtjsUIKBBER\nERE96tF4E20/Xq83BhbYfomkebb3KpSrrYWLk4H7qN7SL+1c7+wXjxUk3WH7xcPc+6XtFzWdaU3a\n+iAu6Te2dyydI9aepPFUZxBMoepQ+QlwbKejJsrLGQQRERERq7oQuEHSZfX6EOCi+oC7n5aLxTZ0\nPYADjwPb2n5M0tJhfqYJnRP5T+y6ZmDnAlna7gZJ77Z9TvdFSe+lapuPtZc3nSPPDrYP7b4gaT8g\nBYKWSAdBRERERB+S9mHFSdvX2b65ZB6A+vC4w4DuwsXlwGeAL9l+W6lssXYkbQN8m6rQM7e+vDew\nMTDN9r2Fck0a7hbwHdvbNZln+YdLs+hfCBAw1faYhiPF0yBpru1Ja7oW5aRAEBEREVGTNM72w/UI\ntVW0oWW+TYULSVNtXynp8H73bV/SdKaRQtJU4KX18lbbVxbOc9Xq7ts+qKks3SQduLr7tq9uKks8\ndZKmAK8A3g98ruvWOOCw0mM0Y4VsMYiIiIhY4SKqg/+GWPmtpSjYMt9TuPgVXWPnJG1ZsHBxIHAl\nVSdDLwMpEAzvIGA28BPbS0qHKVUAWJMUAAbGs4GxVM+fm3Zdfxh4c5FE0Vc6CCIiIiK6SBKwo+3F\npbN0SPqO7YMl/Zo+hQvb2es/wkg6GjiA6rC2R6jGCc62fdlqf7ABkl5BNRlj+ctE218pFojl+9RP\nBsZT5crf/RFI0njbd9XfjwLG2n64cKzokgJBRERERA9Ji2xPLJ2jWxsLFx2STuhz+SGqyQrzm84z\nkkh6HvBW4APAFrY3XcOPPNN5LgBeBMwHltWXbfu4cqmWTxb5B6runk4ubN9fLFSss3pU6/uo/gxv\notpicIbtTxcNFsulQBARERHRQ9L5wNm2byqdpVsbCxew/B/9+wCz6ksHAwup3kLPtH1aoWitJenL\nwATgXqrugWuBubafKJzrNmCCW/aQIOkG2y8vnSOeHknzbe8p6W3AJODDVIXE3QtHi1rOIIiIiIhY\n1cuBIyXdCSxhRTtz6X/EzpU0uW2FC2AHYJLtRwEkTQe+C7yS6o1vCgSr+hNgNPAg8ADwu9LFgdot\nwPOAu0sH6XGVpE9TnWuxfKSn7bnD/0i00EaSNgKmURVhH5fUqmLUhi4FgoiIiIhVvQ7YgmqPOFSH\nyT1YLs5ybS1cbEPXQxvwOLCt7cckLR3mZzZotg8DkPSnVH/frpI02vYOZZOxFfBTSTey8oP4ocP/\nSCM63QP7dF0zMLVAlnjqvgjcCSwAZksaT3VQYbRECgQRERERq5oGvIvqbaWAC4BzgLNKhqK9hYsL\ngRskdQ7YOwS4SNIY4KflYrWXpIOp/hxfCWxONQ3imqKhKieXDtBPW6csxLqxfSZwZteluyTlz7ZF\ncgZBRERERA9JC4EpnfFz9YPunNJv6iUdz8qFi2nAObZLFy6QtA+wX728zvbNJfO0naSzqQoC19j+\n79J52k7SP/W7bvsTTWeJdSfpSNtfHeZAU2x/tulM0V86CCIiIiJWJbpOSq+/V6Es3d4J7NtVuPgU\nMIdCnQ2Sxtl+WNKWwK/qX517W9p+oESukcD2MZK2BSZLmgTcaPu+0rkkPcKKUZrPBjYCltgeVy4V\nUG2p6diE6iDM2wpliXU3pv5adEpHrFkKBBERERGrmkHVMn9pvZ4GnFswT0fbChcXUT2oDbHioRLq\nsxGAzKgfhqS3AKcDP6b6/TpL0om2Ly6Zq3vMYj1a8y+Bfcslqtj+TPda0unA9wvFiXVk+4v114+X\nzhKrly0GEREREX3Ub3X3r5fX2J5XMg9A3Z77dqC7cHGe7X8rmEnAjrYXl8owEklaALym0zUgaWvg\nh7b3KJtsVZLm2d6rdI5ukrYAbrL94tJZYs0knbm6+7aPaypLrF46CCIiIiL6qMentWqEmu3PSvox\nKwoXR5cuXNi2pO8CE0vmGIFG9WwpuB8YVSpMh6TDu5ajqKYG/KFQnOUkLWJFl8poYGvglHKJYh0N\ndX3/cWB6qSCxeukgiIiIiIinRdL5VDPNbyqdZaSQ9Glgd+Br9aUjgEW2P1guFUia0bV8gmok3Zds\n/0+ZRJV6HF7HE8C9wMad8zhi5GhjR0qskAJBRERERDwtkm4HdqF6mFxCfQZB6akPbVe/re/exnLp\n6v77EupW/r+3/S8FMzwf2A5YaPuPkrYB3g+8w/b2pXLFUyNpru1JpXNEf9liEBERERFP1+uALYAD\n6vVs4MFycUYG25dQjawEQNJi2zuVyCJpR+BjwPZUZ1x8HfgEcBQruhxK5Ho/8FHgDmBjSf8H+BTw\nFWDvUrkiBlUKBBERERHxdE0D3kX1sCvgAuAcCo1fHMFKTqT4CnA18C3g9cDNwHxgd9v3FMz1HmA3\n2w9I2gn4ObCf7aE1/Fy0SM/4zOdKerhzi6rbqPQYzahli0FEREREPC2SFgJTOvvBJY0B5mSLwbop\n3EGwoHuCgqT/Anay/WSJPF05VmpH780ZEetXOggiIiIi4ukSsKxrvYyyb8Nbqx5V2fcWMLbJLKsE\nqM4b6Py53Q9sVo+xxPYDhWLt0DMib7vudcbjRaxfKRBERERExNM1A7hBUueQvWnAuQXztNmmq7l3\nRmMpVrUZ1Si67sJOZ8yngZ0bT1Q5sWedrQURz6BsMYiIiIiIp03SJFY+kX9eyTwjnaSTbJ9aOkdE\nbFhSIIiIiIiIaJlSo+Ak/cj2q9d0rWmSZrHikLuOh6gOU/yi7T80nypi8GSLQURERERE+zR6hoOk\nTYAxwFY9ZxGMA57fZJZh/ArYmhUjF48AHgF2pZqYcVShXBEDJQWCiIiIiIj2abrN973A+4HtWfks\ngoeBsxvO0s8rbE/uWs+SdJPtyZJuLZYqYsCkQBARERER0T6NdhDYPkPS2cBHbJ/S5GevpbGSdrK9\nGEDSTqyY+vDHcrEiBksKBBERERER7TOz6Q+0vUzS4UAbCwT/CFwr6ZdUxZMXAn8vaQxwftFkEQMk\nhxRGRERERDRM0guBY4EX0PXSzvahpTIBSDodmANc4pY9KEjaGHhJvfxZDiaMWP9SIIiIiIiIaJik\nBcC5wCLgyc5121cXCwVIeoTqsMJlwGNUb+tte1zJXACSXsGqBZWvFAsUMYCyxSAiIiIionl/sH1m\n6RC9bG9aOkM/ki4AXgTMpypeQHWQYwoEEetROggiIiIiIhom6W+AXYAfAEs7123PLRaqJulQ4JX1\n8se2v1MyD4Ck24AJbdv2EDFo0kEQEREREdG8icBRwFRWbDFwvS5G0r8Ck4EL60vHS9rP9kkFYwHc\nAjwPuLtwjoiBlg6CiIiIiIiGSbqD6o14q0b0SVoI7Gn7yXo9Gphne/fCua4C9gRuZOWOi6KHOkYM\nmnQQREREREQ07xZgc+C+0kH62Bx4oP5+s5JBupxcOkDEhiAFgoiIiIiI5m0O3C7pJtr1RvxUYF79\nxl5UZxF8uGykarqDpG2ptj8A3Gi7jcWViBEtWwwiIiIiIhom6cB+10uPOQSQtB0rP4jfUzIPgKS3\nAp8GfkxVuDgAONH2xSVzRQyaFAgiIiIiImI5Sc8HxtPVbWx7drlEIGkB8JpO14CkrYEf2t6jZK6I\nQZMtBhERERERDZH0CNW0AtVfl98CbHtckWCdENKngCOAW1l5ukLRAgEwqmdLwf3AqFJhIgZVCgQR\nEREREQ2xvWnpDGswDdjN9tI1/pfN+p6k7wNfq9dHAP9ZME/EQMoWg4iIiIiIBtWjA2+1/ZLSWXpJ\n+k/gLbYfLZ2ll6TDgf3r5TW2Ly2ZJ2IQpUAQEREREdEwSZcBx9peXDoLgKSzqLYSPB/YA/gRK09X\nOK5QtGFJWmx7p9I5IgZJthhERERERDRvC+BWSTcCSzoXC445vLn+OgRcXijDulLpABGDJh0EERER\n5TOsGQAACphJREFUERENa/OYQwBJGwEvA37bczhga6SDIGL9SwdBRERERETD2lII6JD0BeAs27dK\n2gyYAywDtpT0AdtfW/3/wjOW64ThbgFjm8wSsSFIgSAiIiIioiFdYw5XuUXZMYcH2H5f/f3RwM9t\nT5P0PKppAUUKBMDqpj6c0ViKiA1ECgQREREREQ1p8ZjDP3Z9/xpgJoDte6RyW/1tf3xt/jtJJ9k+\n9ZnOEzHoRpUOEBERERGxoZG0ZZ9fGxWM9KCkgyXtBewHfK/O+SzgOQVzra23lA4QMQjSQRARERER\n0by5wI7A76m2F2wO3CPpXuDdtocazvNe4EzgecD7bd9TX3818N2GszwVmWgQsR5kikFERERERMMk\nnQNcbPv79fq1wJuAGcAZtl9eMt9w2trKL2mu7Umlc0SMdNliEBERERHRvH07xQEA2z8Apti+Hti4\nXKw1amsrfzoIItaDbDGIiIiIiGje3ZI+BHy9Xh8B3CtpNPBkuVhr1NYH8ZmlA0QMgmwxiIiIiIho\nmKStgOnA/lRjD68DPgE8BOxk+46C8YZVqpVf0guBY4EX0PWS0/ahTWeJGGQpEEREREREtIyks2wf\nWzpHL0nzbO9V4HMXAOcCi+jqsLB9ddNZIgZZthhERERERLTPfqUDDKNUK/8fbJ9Z6LMjNhjpIIiI\niIiIaJm08q9M0t8AuwA/AJZ2rtueWyxUxABKB0FERERERHR8m6qVfxbtOixxInAUMJUVuVyvI2I9\nSYEgIiIiIqJ9Sk0LaGsr/1uAnW3/sXSQiEGWAkFERERERPucUepzJU2nfa38twCbA/cVzhEx0FIg\niIiIiIhoiKRZVK3xfXX2+ts+r6lMPdrayr85cLukm1i5cJExhxHrUQ4pjIiIiIhoiKQDV3e/9Ng+\nSXcAE9rWyj/c71vp36+IQZMCQUREREREACDp28B7bKeVP2IDlC0GERERERENk7QLcCowAdikc932\nzsVCVVrVyi/pEaotDmLlrRmqYnlciVwRgyoFgoiIiIiI5s0ApgOfAw4CjgZGFU1UmV46QDfbm5bO\nELEhyRaDiIiIiIiGSRqyvbekRbYndl8rna1tJI0GbrX9ktJZIgZdOggiIiIiIpq3VNIo4BeSjgF+\nC4wtFabNrfy2l0n6maSdbC8ulSNiQ5AOgoiIiIiIhkmaDNxGtef/FGAz4DTb1xcN1lKSZgN7ATcC\nSzrXM+YwYv1KgSAiIiIiIlrdyp8xhxHNyBaDiIiIiIiGSdoVOBEYT9e/yW1PLZWpza38KQRENCMF\ngoiIiIiI5s0EvgCcAywrnKXbFsCtklrRyt91NsIqt8iYw4j1LlsMIiIiIiIa1taJBWnlj9iwpUAQ\nEREREdEwSScD9wGXAks7120/UCpTm0nass/lR2w/3niYiAGWAkFERERERMMk/brPZdveufEwtL+V\nX9KdwI7A7+tMmwP3APcC77Y9VC5dxOBIgSAiIiIiIlpN0jnAxba/X69fC7wJmAGcYfvlJfNFDIoU\nCCIiIiIiGiJpqu0rJR3e777tS5rO1K2trfySFtme2HNtoe3dJc23vWepbBGDJFMMIiIiIiKacyBw\nJXBIn3sGihYIgLn0aeWXVLqV/25JHwK+Xq+PAO6VNBp4slCmiIGTDoKIiIiIiADa28ovaStgOrA/\nVSHlOuATwEPATrbvKJErYtCkQBARERER0TBJJ/S5/BAwZHt+03k6Rmorv6SzbB9bOkfESJctBhER\nERERzdun/jWrXh8MLATeJ2mm7dMK5Rqprfz7lQ4QMQjSQRARERER0TBJs4E32n60Xo8Fvgu8nqqL\nYEKhXCOylV/SXNuTSueIGOnSQRARERER0bxtgKVd68eBbW0/JmnpMD/zjLP9O2C4Vv070sofMdhS\nIIiIiIiIaN6FwA2SLqvXhwAXSRoD/LRcrDVqayu/SgeIGATZYhARERERUYCkfVjxwH2d7ZtL5lkb\nbW3ll/QO2+eVzhEx0qVAEBERERHREEnjbD8sact+920/0HSmddF0gUDSLKqzEPqyfWhTWSI2BNli\nEBERERHRnIuoJhYMsfKDr+r1ziVCrYOmW/lPb/jzIjZo6SCIiIiIiGiQJAE72l5cOsu6Sit/xGBL\ngSAiIiIiomGSFtmeWDpHR9tb+SXtApwKTAA26Vy33faOi4gRJVsMIiIiIiKaN1fSZNs3lQ5Sa3sr\n/wxgOvA54CDgaGBU0UQRAygdBBERERERDZN0O7ALcCewhPoMAtu7l8zVVpKGbO/d3XnRuVY6W8Qg\nSQdBRERERETzXgdsARxQr2cDD5aLU2lxK/9SSaOAX0g6BvgtMLZwpoiBk7aciIiIiIjmTQMuALYC\ntq6/b8PIvhnA54EnqFr5vwJ8tWiiyvHAc4HjgL2Bo4C3F00UMYCyxSAiIiIiomGSFgJTbC+p12OA\nOaW3GKSVP2LDli0GERERERHNE7Csa72svlZaK1v5Je0KnAiMp+sZxvbUYqEiBlAKBBERERERzZsB\n3CDp0no9DTi3YJ6O7lb+U4CptKOVfybwBeAcVi6sRMR6lC0GEREREREFSJoE7F8vr7E9r2SeNss2\nh4hmpEAQERERERFAe1v5JZ0M3AdcCiztXLf9QKlMEYMoBYKIiIiIiABA0gKqVv4hulr5bQ8VCwVI\n+nWfy27B+MWIgZICQUREREREAGnlj9jQpUAQERERERFA+1r5JU21faWkw/vdt31J05kiBlmmGERE\nREREREdnYsGJXdcMlGrlPxC4Ejikzz0DKRBErEfpIIiIiIiIiIiIdBBERERERGzo2t7KL+mEPpcf\nAoZsz286T8SgSoEgIiIiIiLa3sq/T/1rVr0+GFgIvE/STNunFUsWMUCyxSAiIiIiIlpN0mzgjbYf\nrddjge8Cr6fqIphQMl/EoEgHQUREREREAK1u5d+GrqkKwOPAtrYfk7R0mJ+JiHWUAkFERERERHS0\ntZX/QuAGSZfV60OAiySNAX5aKFPEwMkWg4iIiIiIANrdyi9pH2C/enmd7ZtLZYkYVOkgiIiIiIiI\njla18ksaZ/thSVsCv6p/de5tafuBpjNFDLIUCCIiIiIioqNtrfwXUW1zGKKaptCher1zgUwRAytb\nDCIiIiIiYrm2tfJLErCj7cUlc0RsCFIgiIiIiIjYwPW08q+idCu/pEW2J5bMELEhyBaDiIiIiIho\neyv/XEmTbd9UOEfEQEsHQUREREREtLqVX9LtwC7AncAS6sKF7d1L5ooYNCkQREREREQE0N5Wfknj\ngS2AA+pLs4EHbd9VLlXE4BlVOkBERERERLTGXEmTS4foYxpwAbAVsHX9/aFFE0UMoHQQREREREQE\n0N5WfkkLgSm2l9TrMcCc0rkiBk0OKYyIiIiIiI7X0aeVv1yc5QQs61ovq69FxHqULQYREREREdHR\n1lb+GcANkk6WdDJwPXBu2UgRgydbDCIiIiIiAmh3K7+kScD+9fIa2/NK5okYRNliEBERERERHa1t\n5bc9F5hbOkfEIEuBICIiIiIiOjqt/JfW62mklT9ig5EtBhERERERsVxa+SM2XCkQRERERERERESm\nGERERERERERECgQRERERERERQQoEEREREREREUEKBBEREREREREB/H8pV3+uQs0mrgAAAABJRU5E\nrkJggg==\n", |
|
|
2076 |
"text/plain": [ |
|
|
2077 |
"<Figure size 1296x432 with 2 Axes>" |
|
|
2078 |
] |
|
|
2079 |
}, |
|
|
2080 |
"metadata": { |
|
|
2081 |
"tags": [] |
|
|
2082 |
} |
|
|
2083 |
} |
|
|
2084 |
] |
|
|
2085 |
}, |
|
|
2086 |
{ |
|
|
2087 |
"cell_type": "code", |
|
|
2088 |
"metadata": { |
|
|
2089 |
"id": "IvWaizMhRK0G", |
|
|
2090 |
"colab_type": "code", |
|
|
2091 |
"outputId": "66fb75c0-19fd-4516-b13c-10997b56d491", |
|
|
2092 |
"colab": { |
|
|
2093 |
"base_uri": "https://localhost:8080/", |
|
|
2094 |
"height": 1000 |
|
|
2095 |
} |
|
|
2096 |
}, |
|
|
2097 |
"source": [ |
|
|
2098 |
"\"\"\" Statistics of the high survival probability population \"\"\"\n", |
|
|
2099 |
"high_surv_df.boxplot(figsize=(18,12), rot=90)\n", |
|
|
2100 |
"low_range_features_high_surv = small_range_Q3_Q1(high_surv_df, threshold, 'High survival probability population')\n", |
|
|
2101 |
"high_surv_df.describe()" |
|
|
2102 |
], |
|
|
2103 |
"execution_count": 0, |
|
|
2104 |
"outputs": [ |
|
|
2105 |
{ |
|
|
2106 |
"output_type": "execute_result", |
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|
2107 |
"data": { |
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2108 |
"text/html": [ |
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2109 |
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" }\n", |
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"\n", |
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" }\n", |
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2119 |
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2120 |
" text-align: right;\n", |
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2121 |
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2122 |
"</style>\n", |
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|
2123 |
"<table border=\"1\" class=\"dataframe\">\n", |
|
|
2124 |
" <thead>\n", |
|
|
2125 |
" <tr style=\"text-align: right;\">\n", |
|
|
2126 |
" <th></th>\n", |
|
|
2127 |
" <th>original_shape_Compactness1</th>\n", |
|
|
2128 |
" <th>original_shape_Compactness2</th>\n", |
|
|
2129 |
" <th>original_shape_Maximum3DDiameter</th>\n", |
|
|
2130 |
" <th>original_shape_SphericalDisproportion</th>\n", |
|
|
2131 |
" <th>original_shape_Sphericity</th>\n", |
|
|
2132 |
" <th>original_shape_SurfaceArea</th>\n", |
|
|
2133 |
" <th>original_shape_SurfaceVolumeRatio</th>\n", |
|
|
2134 |
" <th>original_shape_VoxelVolume</th>\n", |
|
|
2135 |
" <th>original_firstorder_Energy</th>\n", |
|
|
2136 |
" <th>original_firstorder_Entropy</th>\n", |
|
|
2137 |
" <th>original_firstorder_Kurtosis</th>\n", |
|
|
2138 |
" <th>original_firstorder_Maximum</th>\n", |
|
|
2139 |
" <th>original_firstorder_Mean</th>\n", |
|
|
2140 |
" <th>original_firstorder_MeanAbsoluteDeviation</th>\n", |
|
|
2141 |
" <th>original_firstorder_Median</th>\n", |
|
|
2142 |
" <th>original_firstorder_Minimum</th>\n", |
|
|
2143 |
" <th>original_firstorder_Range</th>\n", |
|
|
2144 |
" <th>original_firstorder_RootMeanSquared</th>\n", |
|
|
2145 |
" <th>original_firstorder_Skewness</th>\n", |
|
|
2146 |
" <th>original_firstorder_StandardDeviation</th>\n", |
|
|
2147 |
" <th>original_firstorder_Uniformity</th>\n", |
|
|
2148 |
" <th>original_firstorder_Variance</th>\n", |
|
|
2149 |
" <th>original_glcm_Autocorrelation</th>\n", |
|
|
2150 |
" <th>original_glcm_ClusterProminence</th>\n", |
|
|
2151 |
" <th>original_glcm_ClusterShade</th>\n", |
|
|
2152 |
" <th>original_glcm_ClusterTendency</th>\n", |
|
|
2153 |
" <th>original_glcm_Contrast</th>\n", |
|
|
2154 |
" <th>original_glcm_Correlation</th>\n", |
|
|
2155 |
" <th>original_glcm_DifferenceEntropy</th>\n", |
|
|
2156 |
" <th>original_glcm_DifferenceAverage</th>\n", |
|
|
2157 |
" <th>original_glcm_JointEnergy</th>\n", |
|
|
2158 |
" <th>original_glcm_JointEntropy</th>\n", |
|
|
2159 |
" <th>original_glcm_Id</th>\n", |
|
|
2160 |
" <th>original_glcm_Idm</th>\n", |
|
|
2161 |
" <th>original_glcm_Imc1</th>\n", |
|
|
2162 |
" <th>original_glcm_Imc2</th>\n", |
|
|
2163 |
" <th>original_glcm_Idmn</th>\n", |
|
|
2164 |
" <th>original_glcm_Idn</th>\n", |
|
|
2165 |
" <th>original_glcm_InverseVariance</th>\n", |
|
|
2166 |
" <th>original_glcm_MaximumProbability</th>\n", |
|
|
2167 |
" <th>original_glcm_SumAverage</th>\n", |
|
|
2168 |
" <th>original_glcm_SumEntropy</th>\n", |
|
|
2169 |
" <th>original_glrlm_ShortRunEmphasis</th>\n", |
|
|
2170 |
" <th>original_glrlm_LongRunEmphasis</th>\n", |
|
|
2171 |
" <th>original_glrlm_GrayLevelNonUniformity</th>\n", |
|
|
2172 |
" <th>original_glrlm_RunLengthNonUniformity</th>\n", |
|
|
2173 |
" <th>original_glrlm_RunPercentage</th>\n", |
|
|
2174 |
" <th>original_glrlm_LowGrayLevelRunEmphasis</th>\n", |
|
|
2175 |
" <th>original_glrlm_HighGrayLevelRunEmphasis</th>\n", |
|
|
2176 |
" <th>original_glrlm_ShortRunLowGrayLevelEmphasis</th>\n", |
|
|
2177 |
" <th>original_glrlm_ShortRunHighGrayLevelEmphasis</th>\n", |
|
|
2178 |
" <th>original_glrlm_LongRunLowGrayLevelEmphasis</th>\n", |
|
|
2179 |
" <th>original_glrlm_LongRunHighGrayLevelEmphasis</th>\n", |
|
|
2180 |
" <th>Mstage</th>\n", |
|
|
2181 |
" <th>Nstage</th>\n", |
|
|
2182 |
" <th>SourceDataset</th>\n", |
|
|
2183 |
" <th>Tstage</th>\n", |
|
|
2184 |
" <th>age</th>\n", |
|
|
2185 |
" <th>Histology_adenocarcinoma</th>\n", |
|
|
2186 |
" <th>Histology_large cell</th>\n", |
|
|
2187 |
" <th>Histology_nos</th>\n", |
|
|
2188 |
" <th>Histology_squamous cell carcinoma</th>\n", |
|
|
2189 |
" </tr>\n", |
|
|
2190 |
" </thead>\n", |
|
|
2191 |
" <tbody>\n", |
|
|
2192 |
" <tr>\n", |
|
|
2193 |
" <th>count</th>\n", |
|
|
2194 |
" <td>49.000000</td>\n", |
|
|
2195 |
" <td>49.000000</td>\n", |
|
|
2196 |
" <td>49.000000</td>\n", |
|
|
2197 |
" <td>49.000000</td>\n", |
|
|
2198 |
" <td>49.000000</td>\n", |
|
|
2199 |
" <td>49.000000</td>\n", |
|
|
2200 |
" <td>49.000000</td>\n", |
|
|
2201 |
" <td>49.000000</td>\n", |
|
|
2202 |
" <td>49.000000</td>\n", |
|
|
2203 |
" <td>49.000000</td>\n", |
|
|
2204 |
" <td>49.000000</td>\n", |
|
|
2205 |
" <td>49.000000</td>\n", |
|
|
2206 |
" <td>49.000000</td>\n", |
|
|
2207 |
" <td>49.000000</td>\n", |
|
|
2208 |
" <td>49.000000</td>\n", |
|
|
2209 |
" <td>49.000000</td>\n", |
|
|
2210 |
" <td>49.000000</td>\n", |
|
|
2211 |
" <td>49.000000</td>\n", |
|
|
2212 |
" <td>49.000000</td>\n", |
|
|
2213 |
" <td>49.000000</td>\n", |
|
|
2214 |
" <td>49.000000</td>\n", |
|
|
2215 |
" <td>49.000000</td>\n", |
|
|
2216 |
" <td>49.000000</td>\n", |
|
|
2217 |
" <td>49.000000</td>\n", |
|
|
2218 |
" <td>49.000000</td>\n", |
|
|
2219 |
" <td>49.000000</td>\n", |
|
|
2220 |
" <td>49.000000</td>\n", |
|
|
2221 |
" <td>49.000000</td>\n", |
|
|
2222 |
" <td>49.000000</td>\n", |
|
|
2223 |
" <td>49.000000</td>\n", |
|
|
2224 |
" <td>49.000000</td>\n", |
|
|
2225 |
" <td>49.000000</td>\n", |
|
|
2226 |
" <td>49.000000</td>\n", |
|
|
2227 |
" <td>49.000000</td>\n", |
|
|
2228 |
" <td>49.000000</td>\n", |
|
|
2229 |
" <td>49.000000</td>\n", |
|
|
2230 |
" <td>49.000000</td>\n", |
|
|
2231 |
" <td>49.000000</td>\n", |
|
|
2232 |
" <td>49.000000</td>\n", |
|
|
2233 |
" <td>49.000000</td>\n", |
|
|
2234 |
" <td>49.000000</td>\n", |
|
|
2235 |
" <td>49.000000</td>\n", |
|
|
2236 |
" <td>49.000000</td>\n", |
|
|
2237 |
" <td>49.000000</td>\n", |
|
|
2238 |
" <td>49.000000</td>\n", |
|
|
2239 |
" <td>49.000000</td>\n", |
|
|
2240 |
" <td>49.000000</td>\n", |
|
|
2241 |
" <td>49.000000</td>\n", |
|
|
2242 |
" <td>49.000000</td>\n", |
|
|
2243 |
" <td>49.000000</td>\n", |
|
|
2244 |
" <td>49.000000</td>\n", |
|
|
2245 |
" <td>49.000000</td>\n", |
|
|
2246 |
" <td>49.000000</td>\n", |
|
|
2247 |
" <td>49.0</td>\n", |
|
|
2248 |
" <td>49.000000</td>\n", |
|
|
2249 |
" <td>49.000000</td>\n", |
|
|
2250 |
" <td>49.000000</td>\n", |
|
|
2251 |
" <td>49.000000</td>\n", |
|
|
2252 |
" <td>49.000000</td>\n", |
|
|
2253 |
" <td>49.000000</td>\n", |
|
|
2254 |
" <td>49.000000</td>\n", |
|
|
2255 |
" <td>49.000000</td>\n", |
|
|
2256 |
" </tr>\n", |
|
|
2257 |
" <tr>\n", |
|
|
2258 |
" <th>mean</th>\n", |
|
|
2259 |
" <td>0.528398</td>\n", |
|
|
2260 |
" <td>0.394370</td>\n", |
|
|
2261 |
" <td>0.154974</td>\n", |
|
|
2262 |
" <td>0.236880</td>\n", |
|
|
2263 |
" <td>0.577242</td>\n", |
|
|
2264 |
" <td>0.083413</td>\n", |
|
|
2265 |
" <td>0.497987</td>\n", |
|
|
2266 |
" <td>0.041246</td>\n", |
|
|
2267 |
" <td>0.051737</td>\n", |
|
|
2268 |
" <td>0.758203</td>\n", |
|
|
2269 |
" <td>0.023363</td>\n", |
|
|
2270 |
" <td>0.116175</td>\n", |
|
|
2271 |
" <td>0.531811</td>\n", |
|
|
2272 |
" <td>0.546354</td>\n", |
|
|
2273 |
" <td>0.624409</td>\n", |
|
|
2274 |
" <td>0.293413</td>\n", |
|
|
2275 |
" <td>0.191130</td>\n", |
|
|
2276 |
" <td>0.505160</td>\n", |
|
|
2277 |
" <td>0.744236</td>\n", |
|
|
2278 |
" <td>0.569388</td>\n", |
|
|
2279 |
" <td>0.101235</td>\n", |
|
|
2280 |
" <td>0.387421</td>\n", |
|
|
2281 |
" <td>0.403906</td>\n", |
|
|
2282 |
" <td>0.117521</td>\n", |
|
|
2283 |
" <td>0.414754</td>\n", |
|
|
2284 |
" <td>0.332789</td>\n", |
|
|
2285 |
" <td>0.251002</td>\n", |
|
|
2286 |
" <td>0.586743</td>\n", |
|
|
2287 |
" <td>0.678464</td>\n", |
|
|
2288 |
" <td>0.441507</td>\n", |
|
|
2289 |
" <td>0.047922</td>\n", |
|
|
2290 |
" <td>0.713074</td>\n", |
|
|
2291 |
" <td>0.246663</td>\n", |
|
|
2292 |
" <td>0.223391</td>\n", |
|
|
2293 |
" <td>0.659944</td>\n", |
|
|
2294 |
" <td>0.754262</td>\n", |
|
|
2295 |
" <td>0.610092</td>\n", |
|
|
2296 |
" <td>0.443823</td>\n", |
|
|
2297 |
" <td>0.305508</td>\n", |
|
|
2298 |
" <td>0.088494</td>\n", |
|
|
2299 |
" <td>0.495902</td>\n", |
|
|
2300 |
" <td>0.740208</td>\n", |
|
|
2301 |
" <td>0.863784</td>\n", |
|
|
2302 |
" <td>0.036450</td>\n", |
|
|
2303 |
" <td>0.027657</td>\n", |
|
|
2304 |
" <td>0.059445</td>\n", |
|
|
2305 |
" <td>0.841460</td>\n", |
|
|
2306 |
" <td>0.076636</td>\n", |
|
|
2307 |
" <td>0.374127</td>\n", |
|
|
2308 |
" <td>0.098085</td>\n", |
|
|
2309 |
" <td>0.397818</td>\n", |
|
|
2310 |
" <td>0.038744</td>\n", |
|
|
2311 |
" <td>0.066823</td>\n", |
|
|
2312 |
" <td>0.0</td>\n", |
|
|
2313 |
" <td>0.086735</td>\n", |
|
|
2314 |
" <td>0.836735</td>\n", |
|
|
2315 |
" <td>0.183673</td>\n", |
|
|
2316 |
" <td>0.519520</td>\n", |
|
|
2317 |
" <td>0.673469</td>\n", |
|
|
2318 |
" <td>0.020408</td>\n", |
|
|
2319 |
" <td>0.020408</td>\n", |
|
|
2320 |
" <td>0.244898</td>\n", |
|
|
2321 |
" </tr>\n", |
|
|
2322 |
" <tr>\n", |
|
|
2323 |
" <th>std</th>\n", |
|
|
2324 |
" <td>0.204048</td>\n", |
|
|
2325 |
" <td>0.207511</td>\n", |
|
|
2326 |
" <td>0.142206</td>\n", |
|
|
2327 |
" <td>0.166281</td>\n", |
|
|
2328 |
" <td>0.199582</td>\n", |
|
|
2329 |
" <td>0.170937</td>\n", |
|
|
2330 |
" <td>0.212652</td>\n", |
|
|
2331 |
" <td>0.113124</td>\n", |
|
|
2332 |
" <td>0.146943</td>\n", |
|
|
2333 |
" <td>0.190434</td>\n", |
|
|
2334 |
" <td>0.043704</td>\n", |
|
|
2335 |
" <td>0.096689</td>\n", |
|
|
2336 |
" <td>0.230091</td>\n", |
|
|
2337 |
" <td>0.198527</td>\n", |
|
|
2338 |
" <td>0.265962</td>\n", |
|
|
2339 |
" <td>0.124187</td>\n", |
|
|
2340 |
" <td>0.101149</td>\n", |
|
|
2341 |
" <td>0.223790</td>\n", |
|
|
2342 |
" <td>0.107704</td>\n", |
|
|
2343 |
" <td>0.179137</td>\n", |
|
|
2344 |
" <td>0.152075</td>\n", |
|
|
2345 |
" <td>0.195204</td>\n", |
|
|
2346 |
" <td>0.210356</td>\n", |
|
|
2347 |
" <td>0.087931</td>\n", |
|
|
2348 |
" <td>0.074341</td>\n", |
|
|
2349 |
" <td>0.180185</td>\n", |
|
|
2350 |
" <td>0.161003</td>\n", |
|
|
2351 |
" <td>0.190586</td>\n", |
|
|
2352 |
" <td>0.181871</td>\n", |
|
|
2353 |
" <td>0.195414</td>\n", |
|
|
2354 |
" <td>0.098254</td>\n", |
|
|
2355 |
" <td>0.203621</td>\n", |
|
|
2356 |
" <td>0.208747</td>\n", |
|
|
2357 |
" <td>0.211367</td>\n", |
|
|
2358 |
" <td>0.147428</td>\n", |
|
|
2359 |
" <td>0.130952</td>\n", |
|
|
2360 |
" <td>0.218249</td>\n", |
|
|
2361 |
" <td>0.214065</td>\n", |
|
|
2362 |
" <td>0.217157</td>\n", |
|
|
2363 |
" <td>0.126437</td>\n", |
|
|
2364 |
" <td>0.207686</td>\n", |
|
|
2365 |
" <td>0.201321</td>\n", |
|
|
2366 |
" <td>0.158136</td>\n", |
|
|
2367 |
" <td>0.080802</td>\n", |
|
|
2368 |
" <td>0.089419</td>\n", |
|
|
2369 |
" <td>0.175886</td>\n", |
|
|
2370 |
" <td>0.185898</td>\n", |
|
|
2371 |
" <td>0.103946</td>\n", |
|
|
2372 |
" <td>0.200209</td>\n", |
|
|
2373 |
" <td>0.116339</td>\n", |
|
|
2374 |
" <td>0.200210</td>\n", |
|
|
2375 |
" <td>0.086753</td>\n", |
|
|
2376 |
" <td>0.096787</td>\n", |
|
|
2377 |
" <td>0.0</td>\n", |
|
|
2378 |
" <td>0.201240</td>\n", |
|
|
2379 |
" <td>0.373438</td>\n", |
|
|
2380 |
" <td>0.259082</td>\n", |
|
|
2381 |
" <td>0.174567</td>\n", |
|
|
2382 |
" <td>0.473804</td>\n", |
|
|
2383 |
" <td>0.142857</td>\n", |
|
|
2384 |
" <td>0.142857</td>\n", |
|
|
2385 |
" <td>0.434483</td>\n", |
|
|
2386 |
" </tr>\n", |
|
|
2387 |
" <tr>\n", |
|
|
2388 |
" <th>min</th>\n", |
|
|
2389 |
" <td>0.070657</td>\n", |
|
|
2390 |
" <td>0.028427</td>\n", |
|
|
2391 |
" <td>0.002937</td>\n", |
|
|
2392 |
" <td>0.029617</td>\n", |
|
|
2393 |
" <td>0.092373</td>\n", |
|
|
2394 |
" <td>0.001760</td>\n", |
|
|
2395 |
" <td>0.053241</td>\n", |
|
|
2396 |
" <td>0.000431</td>\n", |
|
|
2397 |
" <td>0.000609</td>\n", |
|
|
2398 |
" <td>0.066163</td>\n", |
|
|
2399 |
" <td>0.002094</td>\n", |
|
|
2400 |
" <td>0.028990</td>\n", |
|
|
2401 |
" <td>0.010927</td>\n", |
|
|
2402 |
" <td>0.058309</td>\n", |
|
|
2403 |
" <td>0.000000</td>\n", |
|
|
2404 |
" <td>0.174419</td>\n", |
|
|
2405 |
" <td>0.075472</td>\n", |
|
|
2406 |
" <td>0.064524</td>\n", |
|
|
2407 |
" <td>0.355840</td>\n", |
|
|
2408 |
" <td>0.130948</td>\n", |
|
|
2409 |
" <td>0.004511</td>\n", |
|
|
2410 |
" <td>0.034069</td>\n", |
|
|
2411 |
" <td>0.072969</td>\n", |
|
|
2412 |
" <td>0.008526</td>\n", |
|
|
2413 |
" <td>0.277496</td>\n", |
|
|
2414 |
" <td>0.027396</td>\n", |
|
|
2415 |
" <td>0.007966</td>\n", |
|
|
2416 |
" <td>0.178999</td>\n", |
|
|
2417 |
" <td>0.090164</td>\n", |
|
|
2418 |
" <td>0.022466</td>\n", |
|
|
2419 |
" <td>0.000840</td>\n", |
|
|
2420 |
" <td>0.031450</td>\n", |
|
|
2421 |
" <td>0.021111</td>\n", |
|
|
2422 |
" <td>0.000551</td>\n", |
|
|
2423 |
" <td>0.145009</td>\n", |
|
|
2424 |
" <td>0.405984</td>\n", |
|
|
2425 |
" <td>0.000000</td>\n", |
|
|
2426 |
" <td>0.000000</td>\n", |
|
|
2427 |
" <td>0.014852</td>\n", |
|
|
2428 |
" <td>0.001273</td>\n", |
|
|
2429 |
" <td>0.111286</td>\n", |
|
|
2430 |
" <td>0.045437</td>\n", |
|
|
2431 |
" <td>0.170472</td>\n", |
|
|
2432 |
" <td>0.000000</td>\n", |
|
|
2433 |
" <td>0.000075</td>\n", |
|
|
2434 |
" <td>0.000847</td>\n", |
|
|
2435 |
" <td>0.068151</td>\n", |
|
|
2436 |
" <td>0.002905</td>\n", |
|
|
2437 |
" <td>0.087059</td>\n", |
|
|
2438 |
" <td>0.003963</td>\n", |
|
|
2439 |
" <td>0.102891</td>\n", |
|
|
2440 |
" <td>0.001823</td>\n", |
|
|
2441 |
" <td>0.006737</td>\n", |
|
|
2442 |
" <td>0.0</td>\n", |
|
|
2443 |
" <td>0.000000</td>\n", |
|
|
2444 |
" <td>0.000000</td>\n", |
|
|
2445 |
" <td>0.000000</td>\n", |
|
|
2446 |
" <td>0.070881</td>\n", |
|
|
2447 |
" <td>0.000000</td>\n", |
|
|
2448 |
" <td>0.000000</td>\n", |
|
|
2449 |
" <td>0.000000</td>\n", |
|
|
2450 |
" <td>0.000000</td>\n", |
|
|
2451 |
" </tr>\n", |
|
|
2452 |
" <tr>\n", |
|
|
2453 |
" <th>25%</th>\n", |
|
|
2454 |
" <td>0.430321</td>\n", |
|
|
2455 |
" <td>0.272667</td>\n", |
|
|
2456 |
" <td>0.062894</td>\n", |
|
|
2457 |
" <td>0.131484</td>\n", |
|
|
2458 |
" <td>0.490475</td>\n", |
|
|
2459 |
" <td>0.014631</td>\n", |
|
|
2460 |
" <td>0.352950</td>\n", |
|
|
2461 |
" <td>0.003349</td>\n", |
|
|
2462 |
" <td>0.006892</td>\n", |
|
|
2463 |
" <td>0.667108</td>\n", |
|
|
2464 |
" <td>0.004981</td>\n", |
|
|
2465 |
" <td>0.060847</td>\n", |
|
|
2466 |
" <td>0.341917</td>\n", |
|
|
2467 |
" <td>0.405357</td>\n", |
|
|
2468 |
" <td>0.401630</td>\n", |
|
|
2469 |
" <td>0.184755</td>\n", |
|
|
2470 |
" <td>0.123213</td>\n", |
|
|
2471 |
" <td>0.331705</td>\n", |
|
|
2472 |
" <td>0.710336</td>\n", |
|
|
2473 |
" <td>0.431955</td>\n", |
|
|
2474 |
" <td>0.024018</td>\n", |
|
|
2475 |
" <td>0.223070</td>\n", |
|
|
2476 |
" <td>0.225167</td>\n", |
|
|
2477 |
" <td>0.053090</td>\n", |
|
|
2478 |
" <td>0.358123</td>\n", |
|
|
2479 |
" <td>0.194806</td>\n", |
|
|
2480 |
" <td>0.114654</td>\n", |
|
|
2481 |
" <td>0.436501</td>\n", |
|
|
2482 |
" <td>0.561502</td>\n", |
|
|
2483 |
" <td>0.289276</td>\n", |
|
|
2484 |
" <td>0.004651</td>\n", |
|
|
2485 |
" <td>0.596031</td>\n", |
|
|
2486 |
" <td>0.093633</td>\n", |
|
|
2487 |
" <td>0.071632</td>\n", |
|
|
2488 |
" <td>0.588443</td>\n", |
|
|
2489 |
" <td>0.681380</td>\n", |
|
|
2490 |
" <td>0.483976</td>\n", |
|
|
2491 |
" <td>0.304503</td>\n", |
|
|
2492 |
" <td>0.132036</td>\n", |
|
|
2493 |
" <td>0.011489</td>\n", |
|
|
2494 |
" <td>0.330578</td>\n", |
|
|
2495 |
" <td>0.643010</td>\n", |
|
|
2496 |
" <td>0.820629</td>\n", |
|
|
2497 |
" <td>0.004838</td>\n", |
|
|
2498 |
" <td>0.000901</td>\n", |
|
|
2499 |
" <td>0.006484</td>\n", |
|
|
2500 |
" <td>0.790131</td>\n", |
|
|
2501 |
" <td>0.028910</td>\n", |
|
|
2502 |
" <td>0.193198</td>\n", |
|
|
2503 |
" <td>0.041229</td>\n", |
|
|
2504 |
" <td>0.234117</td>\n", |
|
|
2505 |
" <td>0.009861</td>\n", |
|
|
2506 |
" <td>0.022517</td>\n", |
|
|
2507 |
" <td>0.0</td>\n", |
|
|
2508 |
" <td>0.000000</td>\n", |
|
|
2509 |
" <td>1.000000</td>\n", |
|
|
2510 |
" <td>0.000000</td>\n", |
|
|
2511 |
" <td>0.416473</td>\n", |
|
|
2512 |
" <td>0.000000</td>\n", |
|
|
2513 |
" <td>0.000000</td>\n", |
|
|
2514 |
" <td>0.000000</td>\n", |
|
|
2515 |
" <td>0.000000</td>\n", |
|
|
2516 |
" </tr>\n", |
|
|
2517 |
" <tr>\n", |
|
|
2518 |
" <th>50%</th>\n", |
|
|
2519 |
" <td>0.534776</td>\n", |
|
|
2520 |
" <td>0.374776</td>\n", |
|
|
2521 |
" <td>0.090684</td>\n", |
|
|
2522 |
" <td>0.199384</td>\n", |
|
|
2523 |
" <td>0.591992</td>\n", |
|
|
2524 |
" <td>0.024362</td>\n", |
|
|
2525 |
" <td>0.490734</td>\n", |
|
|
2526 |
" <td>0.006853</td>\n", |
|
|
2527 |
" <td>0.015420</td>\n", |
|
|
2528 |
" <td>0.816523</td>\n", |
|
|
2529 |
" <td>0.009787</td>\n", |
|
|
2530 |
" <td>0.095572</td>\n", |
|
|
2531 |
" <td>0.527256</td>\n", |
|
|
2532 |
" <td>0.555142</td>\n", |
|
|
2533 |
" <td>0.695576</td>\n", |
|
|
2534 |
" <td>0.262274</td>\n", |
|
|
2535 |
" <td>0.183248</td>\n", |
|
|
2536 |
" <td>0.533637</td>\n", |
|
|
2537 |
" <td>0.759095</td>\n", |
|
|
2538 |
" <td>0.561907</td>\n", |
|
|
2539 |
" <td>0.038216</td>\n", |
|
|
2540 |
" <td>0.352343</td>\n", |
|
|
2541 |
" <td>0.389153</td>\n", |
|
|
2542 |
" <td>0.103868</td>\n", |
|
|
2543 |
" <td>0.424058</td>\n", |
|
|
2544 |
" <td>0.314301</td>\n", |
|
|
2545 |
" <td>0.223802</td>\n", |
|
|
2546 |
" <td>0.578004</td>\n", |
|
|
2547 |
" <td>0.728681</td>\n", |
|
|
2548 |
" <td>0.473422</td>\n", |
|
|
2549 |
" <td>0.010676</td>\n", |
|
|
2550 |
" <td>0.768585</td>\n", |
|
|
2551 |
" <td>0.181275</td>\n", |
|
|
2552 |
" <td>0.142793</td>\n", |
|
|
2553 |
" <td>0.681695</td>\n", |
|
|
2554 |
" <td>0.774210</td>\n", |
|
|
2555 |
" <td>0.579555</td>\n", |
|
|
2556 |
" <td>0.377826</td>\n", |
|
|
2557 |
" <td>0.247922</td>\n", |
|
|
2558 |
" <td>0.035262</td>\n", |
|
|
2559 |
" <td>0.510042</td>\n", |
|
|
2560 |
" <td>0.801875</td>\n", |
|
|
2561 |
" <td>0.921739</td>\n", |
|
|
2562 |
" <td>0.010358</td>\n", |
|
|
2563 |
" <td>0.002527</td>\n", |
|
|
2564 |
" <td>0.011608</td>\n", |
|
|
2565 |
" <td>0.914334</td>\n", |
|
|
2566 |
" <td>0.042803</td>\n", |
|
|
2567 |
" <td>0.341007</td>\n", |
|
|
2568 |
" <td>0.058815</td>\n", |
|
|
2569 |
" <td>0.367166</td>\n", |
|
|
2570 |
" <td>0.017114</td>\n", |
|
|
2571 |
" <td>0.039258</td>\n", |
|
|
2572 |
" <td>0.0</td>\n", |
|
|
2573 |
" <td>0.000000</td>\n", |
|
|
2574 |
" <td>1.000000</td>\n", |
|
|
2575 |
" <td>0.000000</td>\n", |
|
|
2576 |
" <td>0.533706</td>\n", |
|
|
2577 |
" <td>1.000000</td>\n", |
|
|
2578 |
" <td>0.000000</td>\n", |
|
|
2579 |
" <td>0.000000</td>\n", |
|
|
2580 |
" <td>0.000000</td>\n", |
|
|
2581 |
" </tr>\n", |
|
|
2582 |
" <tr>\n", |
|
|
2583 |
" <th>75%</th>\n", |
|
|
2584 |
" <td>0.656266</td>\n", |
|
|
2585 |
" <td>0.511193</td>\n", |
|
|
2586 |
" <td>0.199957</td>\n", |
|
|
2587 |
" <td>0.272924</td>\n", |
|
|
2588 |
" <td>0.704737</td>\n", |
|
|
2589 |
" <td>0.064233</td>\n", |
|
|
2590 |
" <td>0.656271</td>\n", |
|
|
2591 |
" <td>0.017747</td>\n", |
|
|
2592 |
" <td>0.028883</td>\n", |
|
|
2593 |
" <td>0.879602</td>\n", |
|
|
2594 |
" <td>0.017262</td>\n", |
|
|
2595 |
" <td>0.123925</td>\n", |
|
|
2596 |
" <td>0.703208</td>\n", |
|
|
2597 |
" <td>0.693611</td>\n", |
|
|
2598 |
" <td>0.864959</td>\n", |
|
|
2599 |
" <td>0.377261</td>\n", |
|
|
2600 |
" <td>0.218982</td>\n", |
|
|
2601 |
" <td>0.671228</td>\n", |
|
|
2602 |
" <td>0.818600</td>\n", |
|
|
2603 |
" <td>0.706582</td>\n", |
|
|
2604 |
" <td>0.115934</td>\n", |
|
|
2605 |
" <td>0.530085</td>\n", |
|
|
2606 |
" <td>0.564948</td>\n", |
|
|
2607 |
" <td>0.154561</td>\n", |
|
|
2608 |
" <td>0.457865</td>\n", |
|
|
2609 |
" <td>0.452166</td>\n", |
|
|
2610 |
" <td>0.379228</td>\n", |
|
|
2611 |
" <td>0.724608</td>\n", |
|
|
2612 |
" <td>0.830862</td>\n", |
|
|
2613 |
" <td>0.623529</td>\n", |
|
|
2614 |
" <td>0.048946</td>\n", |
|
|
2615 |
" <td>0.865874</td>\n", |
|
|
2616 |
" <td>0.349531</td>\n", |
|
|
2617 |
" <td>0.327083</td>\n", |
|
|
2618 |
" <td>0.763775</td>\n", |
|
|
2619 |
" <td>0.834138</td>\n", |
|
|
2620 |
" <td>0.764391</td>\n", |
|
|
2621 |
" <td>0.548064</td>\n", |
|
|
2622 |
" <td>0.440099</td>\n", |
|
|
2623 |
" <td>0.122947</td>\n", |
|
|
2624 |
" <td>0.645167</td>\n", |
|
|
2625 |
" <td>0.880065</td>\n", |
|
|
2626 |
" <td>0.962616</td>\n", |
|
|
2627 |
" <td>0.031135</td>\n", |
|
|
2628 |
" <td>0.006254</td>\n", |
|
|
2629 |
" <td>0.029610</td>\n", |
|
|
2630 |
" <td>0.957657</td>\n", |
|
|
2631 |
" <td>0.088221</td>\n", |
|
|
2632 |
" <td>0.513287</td>\n", |
|
|
2633 |
" <td>0.118922</td>\n", |
|
|
2634 |
" <td>0.573665</td>\n", |
|
|
2635 |
" <td>0.032011</td>\n", |
|
|
2636 |
" <td>0.061394</td>\n", |
|
|
2637 |
" <td>0.0</td>\n", |
|
|
2638 |
" <td>0.000000</td>\n", |
|
|
2639 |
" <td>1.000000</td>\n", |
|
|
2640 |
" <td>0.250000</td>\n", |
|
|
2641 |
" <td>0.599433</td>\n", |
|
|
2642 |
" <td>1.000000</td>\n", |
|
|
2643 |
" <td>0.000000</td>\n", |
|
|
2644 |
" <td>0.000000</td>\n", |
|
|
2645 |
" <td>0.000000</td>\n", |
|
|
2646 |
" </tr>\n", |
|
|
2647 |
" <tr>\n", |
|
|
2648 |
" <th>max</th>\n", |
|
|
2649 |
" <td>0.905877</td>\n", |
|
|
2650 |
" <td>0.851042</td>\n", |
|
|
2651 |
" <td>0.653763</td>\n", |
|
|
2652 |
" <td>0.780239</td>\n", |
|
|
2653 |
" <td>0.922113</td>\n", |
|
|
2654 |
" <td>1.000000</td>\n", |
|
|
2655 |
" <td>0.924444</td>\n", |
|
|
2656 |
" <td>0.564364</td>\n", |
|
|
2657 |
" <td>1.000000</td>\n", |
|
|
2658 |
" <td>0.966960</td>\n", |
|
|
2659 |
" <td>0.264958</td>\n", |
|
|
2660 |
" <td>0.528194</td>\n", |
|
|
2661 |
" <td>0.907618</td>\n", |
|
|
2662 |
" <td>0.922701</td>\n", |
|
|
2663 |
" <td>0.949942</td>\n", |
|
|
2664 |
" <td>0.598191</td>\n", |
|
|
2665 |
" <td>0.587193</td>\n", |
|
|
2666 |
" <td>1.000000</td>\n", |
|
|
2667 |
" <td>0.908030</td>\n", |
|
|
2668 |
" <td>0.919687</td>\n", |
|
|
2669 |
" <td>0.791420</td>\n", |
|
|
2670 |
" <td>0.856806</td>\n", |
|
|
2671 |
" <td>0.856777</td>\n", |
|
|
2672 |
" <td>0.364704</td>\n", |
|
|
2673 |
" <td>0.642372</td>\n", |
|
|
2674 |
" <td>0.807656</td>\n", |
|
|
2675 |
" <td>0.575810</td>\n", |
|
|
2676 |
" <td>0.893988</td>\n", |
|
|
2677 |
" <td>0.907959</td>\n", |
|
|
2678 |
" <td>0.749105</td>\n", |
|
|
2679 |
" <td>0.568396</td>\n", |
|
|
2680 |
" <td>0.970061</td>\n", |
|
|
2681 |
" <td>0.958317</td>\n", |
|
|
2682 |
" <td>0.953010</td>\n", |
|
|
2683 |
" <td>0.862182</td>\n", |
|
|
2684 |
" <td>0.992465</td>\n", |
|
|
2685 |
" <td>0.986456</td>\n", |
|
|
2686 |
" <td>0.950508</td>\n", |
|
|
2687 |
" <td>0.847394</td>\n", |
|
|
2688 |
" <td>0.586096</td>\n", |
|
|
2689 |
" <td>0.892450</td>\n", |
|
|
2690 |
" <td>0.969727</td>\n", |
|
|
2691 |
" <td>1.000000</td>\n", |
|
|
2692 |
" <td>0.524767</td>\n", |
|
|
2693 |
" <td>0.555568</td>\n", |
|
|
2694 |
" <td>1.000000</td>\n", |
|
|
2695 |
" <td>1.000000</td>\n", |
|
|
2696 |
" <td>0.641848</td>\n", |
|
|
2697 |
" <td>0.838058</td>\n", |
|
|
2698 |
" <td>0.673589</td>\n", |
|
|
2699 |
" <td>0.851650</td>\n", |
|
|
2700 |
" <td>0.599560</td>\n", |
|
|
2701 |
" <td>0.620896</td>\n", |
|
|
2702 |
" <td>0.0</td>\n", |
|
|
2703 |
" <td>0.750000</td>\n", |
|
|
2704 |
" <td>1.000000</td>\n", |
|
|
2705 |
" <td>1.000000</td>\n", |
|
|
2706 |
" <td>0.906940</td>\n", |
|
|
2707 |
" <td>1.000000</td>\n", |
|
|
2708 |
" <td>1.000000</td>\n", |
|
|
2709 |
" <td>1.000000</td>\n", |
|
|
2710 |
" <td>1.000000</td>\n", |
|
|
2711 |
" </tr>\n", |
|
|
2712 |
" </tbody>\n", |
|
|
2713 |
"</table>\n", |
|
|
2714 |
"</div>" |
|
|
2715 |
], |
|
|
2716 |
"text/plain": [ |
|
|
2717 |
" original_shape_Compactness1 ... Histology_squamous cell carcinoma\n", |
|
|
2718 |
"count 49.000000 ... 49.000000\n", |
|
|
2719 |
"mean 0.528398 ... 0.244898\n", |
|
|
2720 |
"std 0.204048 ... 0.434483\n", |
|
|
2721 |
"min 0.070657 ... 0.000000\n", |
|
|
2722 |
"25% 0.430321 ... 0.000000\n", |
|
|
2723 |
"50% 0.534776 ... 0.000000\n", |
|
|
2724 |
"75% 0.656266 ... 0.000000\n", |
|
|
2725 |
"max 0.905877 ... 1.000000\n", |
|
|
2726 |
"\n", |
|
|
2727 |
"[8 rows x 62 columns]" |
|
|
2728 |
] |
|
|
2729 |
}, |
|
|
2730 |
"metadata": { |
|
|
2731 |
"tags": [] |
|
|
2732 |
}, |
|
|
2733 |
"execution_count": 23 |
|
|
2734 |
}, |
|
|
2735 |
{ |
|
|
2736 |
"output_type": "display_data", |
|
|
2737 |
"data": { |
|
|
2738 |
"image/png": 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PzjpQANitPM8jz/N48OxLw9sAANy9SaYz/HxE/Eme53+a5/mPI+J3IuJzWx6TR8S/997t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9Es/Xiv0NknKWC7CKimhChLYOxWvgvhcXw/owcLeZ7RfbRkP7Tfn4Q2VC5EbuQhtLhzaq9Kxr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\nzGw5edxrttzG3ZI+y1I5xIlU5RAR8cb1ueK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|
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|
2739 |
"text/plain": [ |
|
|
2740 |
"<Figure size 1296x864 with 1 Axes>" |
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2741 |
] |
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|
2742 |
}, |
|
|
2743 |
"metadata": { |
|
|
2744 |
"tags": [] |
|
|
2745 |
} |
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|
2746 |
}, |
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|
2747 |
{ |
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|
2748 |
"output_type": "display_data", |
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|
2749 |
"data": { |
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2750 |
"image/png": 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DgevJyYXq/ufnwEuK7vOAs8nHsDuArwEH1ZS7Y989xTy6yfvw7cX+8LYmy7AfcDH5WPlz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\n8P8BCw3y7powTZ8AAAAASUVORK5CYII=\n", |
|
|
2751 |
"text/plain": [ |
|
|
2752 |
"<Figure size 1296x432 with 2 Axes>" |
|
|
2753 |
] |
|
|
2754 |
}, |
|
|
2755 |
"metadata": { |
|
|
2756 |
"tags": [] |
|
|
2757 |
} |
|
|
2758 |
} |
|
|
2759 |
] |
|
|
2760 |
}, |
|
|
2761 |
{ |
|
|
2762 |
"cell_type": "code", |
|
|
2763 |
"metadata": { |
|
|
2764 |
"id": "Cs7SKsA8UjBJ", |
|
|
2765 |
"colab_type": "code", |
|
|
2766 |
"outputId": "8c77038f-e9af-4f12-aeb4-93d4160361c8", |
|
|
2767 |
"colab": { |
|
|
2768 |
"base_uri": "https://localhost:8080/", |
|
|
2769 |
"height": 71 |
|
|
2770 |
} |
|
|
2771 |
}, |
|
|
2772 |
"source": [ |
|
|
2773 |
"\"\"\" Intersection of low quantile range features extracted from both population \"\"\"\n", |
|
|
2774 |
"# print(low_range_features_low_surv)\n", |
|
|
2775 |
"# print(low_range_features_high_surv)\n", |
|
|
2776 |
"intersection_features = list(set(low_range_features_low_surv) & set(low_range_features_high_surv))\n", |
|
|
2777 |
"union_features = list(low_range_features_low_surv) + list(set(low_range_features_high_surv) - set(low_range_features_low_surv))\n", |
|
|
2778 |
"print('Features with low range in both population', intersection_features)\n", |
|
|
2779 |
"print('Features with low range in one of the population', union_features)" |
|
|
2780 |
], |
|
|
2781 |
"execution_count": 0, |
|
|
2782 |
"outputs": [ |
|
|
2783 |
{ |
|
|
2784 |
"output_type": "stream", |
|
|
2785 |
"text": [ |
|
|
2786 |
"Features with low range in both population ['original_glcm_ClusterShade', 'original_glrlm_LongRunLowGrayLevelEmphasis', 'SourceDataset', 'Mstage', 'original_firstorder_Energy', 'Histology_large cell', 'original_glrlm_LowGrayLevelRunEmphasis', 'original_glrlm_ShortRunLowGrayLevelEmphasis', 'Histology_nos']\n", |
|
|
2787 |
"Features with low range in one of the population ['original_firstorder_Energy', 'original_firstorder_Median', 'original_glcm_ClusterShade', 'original_glcm_Contrast', 'original_glrlm_LowGrayLevelRunEmphasis', 'original_glrlm_ShortRunLowGrayLevelEmphasis', 'original_glrlm_LongRunLowGrayLevelEmphasis', 'Mstage', 'SourceDataset', 'Histology_adenocarcinoma', 'Histology_large cell', 'Histology_nos', 'original_glrlm_RunLengthNonUniformity', 'original_firstorder_Maximum', 'original_glrlm_LongRunHighGrayLevelEmphasis', 'original_glrlm_LongRunEmphasis', 'original_firstorder_Range', 'original_shape_VoxelVolume', 'Nstage', 'original_glcm_JointEnergy', 'original_shape_SurfaceArea', 'original_glrlm_GrayLevelNonUniformity', 'original_firstorder_Uniformity', 'original_firstorder_Kurtosis', 'Histology_squamous cell carcinoma']\n" |
|
|
2788 |
], |
|
|
2789 |
"name": "stdout" |
|
|
2790 |
} |
|
|
2791 |
] |
|
|
2792 |
}, |
|
|
2793 |
{ |
|
|
2794 |
"cell_type": "code", |
|
|
2795 |
"metadata": { |
|
|
2796 |
"id": "nJqZBc8KgH1m", |
|
|
2797 |
"colab_type": "code", |
|
|
2798 |
"outputId": "1b32e3ff-db9e-4edb-8852-01e8a46a37ac", |
|
|
2799 |
"colab": { |
|
|
2800 |
"base_uri": "https://localhost:8080/", |
|
|
2801 |
"height": 824 |
|
|
2802 |
} |
|
|
2803 |
}, |
|
|
2804 |
"source": [ |
|
|
2805 |
"\"\"\" Comparing the mean value of the features in both populations \"\"\"\n", |
|
|
2806 |
"feat_ = union_features\n", |
|
|
2807 |
"s1 = low_surv_df[feat_].describe().loc['mean']\n", |
|
|
2808 |
"s2 = low_surv_df[feat_].describe().loc['std']\n", |
|
|
2809 |
"s3 = low_surv_df[feat_].describe().loc['75%'] - low_surv_df[feat_].describe().loc['25%']\n", |
|
|
2810 |
"s4 = high_surv_df[feat_].describe().loc['mean']\n", |
|
|
2811 |
"s5 = high_surv_df[feat_].describe().loc['std']\n", |
|
|
2812 |
"s6 = high_surv_df[feat_].describe().loc['75%'] - high_surv_df[feat_].describe().loc['25%']\n", |
|
|
2813 |
"\n", |
|
|
2814 |
"df = pd.concat([s1, s2, s3, s4, s5, s6], axis=1)\n", |
|
|
2815 |
"df.columns = ['low_surv_df_mean', 'low_surv_std', 'low_surve_Q3Q1_range', 'high_surv_df_mean', 'high_surv_std', 'high_surv_Q3Q1_range']\n", |
|
|
2816 |
"df" |
|
|
2817 |
], |
|
|
2818 |
"execution_count": 0, |
|
|
2819 |
"outputs": [ |
|
|
2820 |
{ |
|
|
2821 |
"output_type": "execute_result", |
|
|
2822 |
"data": { |
|
|
2823 |
"text/html": [ |
|
|
2824 |
"<div>\n", |
|
|
2825 |
"<style scoped>\n", |
|
|
2826 |
" .dataframe tbody tr th:only-of-type {\n", |
|
|
2827 |
" vertical-align: middle;\n", |
|
|
2828 |
" }\n", |
|
|
2829 |
"\n", |
|
|
2830 |
" .dataframe tbody tr th {\n", |
|
|
2831 |
" vertical-align: top;\n", |
|
|
2832 |
" }\n", |
|
|
2833 |
"\n", |
|
|
2834 |
" .dataframe thead th {\n", |
|
|
2835 |
" text-align: right;\n", |
|
|
2836 |
" }\n", |
|
|
2837 |
"</style>\n", |
|
|
2838 |
"<table border=\"1\" class=\"dataframe\">\n", |
|
|
2839 |
" <thead>\n", |
|
|
2840 |
" <tr style=\"text-align: right;\">\n", |
|
|
2841 |
" <th></th>\n", |
|
|
2842 |
" <th>low_surv_df_mean</th>\n", |
|
|
2843 |
" <th>low_surv_std</th>\n", |
|
|
2844 |
" <th>low_surve_Q3Q1_range</th>\n", |
|
|
2845 |
" <th>high_surv_df_mean</th>\n", |
|
|
2846 |
" <th>high_surv_std</th>\n", |
|
|
2847 |
" <th>high_surv_Q3Q1_range</th>\n", |
|
|
2848 |
" </tr>\n", |
|
|
2849 |
" </thead>\n", |
|
|
2850 |
" <tbody>\n", |
|
|
2851 |
" <tr>\n", |
|
|
2852 |
" <th>original_firstorder_Energy</th>\n", |
|
|
2853 |
" <td>0.061725</td>\n", |
|
|
2854 |
" <td>0.085546</td>\n", |
|
|
2855 |
" <td>0.054420</td>\n", |
|
|
2856 |
" <td>0.051737</td>\n", |
|
|
2857 |
" <td>0.146943</td>\n", |
|
|
2858 |
" <td>0.021991</td>\n", |
|
|
2859 |
" </tr>\n", |
|
|
2860 |
" <tr>\n", |
|
|
2861 |
" <th>original_firstorder_Median</th>\n", |
|
|
2862 |
" <td>0.858435</td>\n", |
|
|
2863 |
" <td>0.161358</td>\n", |
|
|
2864 |
" <td>0.077998</td>\n", |
|
|
2865 |
" <td>0.624409</td>\n", |
|
|
2866 |
" <td>0.265962</td>\n", |
|
|
2867 |
" <td>0.463329</td>\n", |
|
|
2868 |
" </tr>\n", |
|
|
2869 |
" <tr>\n", |
|
|
2870 |
" <th>original_glcm_ClusterShade</th>\n", |
|
|
2871 |
" <td>0.387256</td>\n", |
|
|
2872 |
" <td>0.100801</td>\n", |
|
|
2873 |
" <td>0.095666</td>\n", |
|
|
2874 |
" <td>0.414754</td>\n", |
|
|
2875 |
" <td>0.074341</td>\n", |
|
|
2876 |
" <td>0.099742</td>\n", |
|
|
2877 |
" </tr>\n", |
|
|
2878 |
" <tr>\n", |
|
|
2879 |
" <th>original_glcm_Contrast</th>\n", |
|
|
2880 |
" <td>0.084823</td>\n", |
|
|
2881 |
" <td>0.111087</td>\n", |
|
|
2882 |
" <td>0.083460</td>\n", |
|
|
2883 |
" <td>0.251002</td>\n", |
|
|
2884 |
" <td>0.161003</td>\n", |
|
|
2885 |
" <td>0.264574</td>\n", |
|
|
2886 |
" </tr>\n", |
|
|
2887 |
" <tr>\n", |
|
|
2888 |
" <th>original_glrlm_LowGrayLevelRunEmphasis</th>\n", |
|
|
2889 |
" <td>0.039752</td>\n", |
|
|
2890 |
" <td>0.106124</td>\n", |
|
|
2891 |
" <td>0.031192</td>\n", |
|
|
2892 |
" <td>0.076636</td>\n", |
|
|
2893 |
" <td>0.103946</td>\n", |
|
|
2894 |
" <td>0.059311</td>\n", |
|
|
2895 |
" </tr>\n", |
|
|
2896 |
" <tr>\n", |
|
|
2897 |
" <th>original_glrlm_ShortRunLowGrayLevelEmphasis</th>\n", |
|
|
2898 |
" <td>0.047246</td>\n", |
|
|
2899 |
" <td>0.107909</td>\n", |
|
|
2900 |
" <td>0.036021</td>\n", |
|
|
2901 |
" <td>0.098085</td>\n", |
|
|
2902 |
" <td>0.116339</td>\n", |
|
|
2903 |
" <td>0.077694</td>\n", |
|
|
2904 |
" </tr>\n", |
|
|
2905 |
" <tr>\n", |
|
|
2906 |
" <th>original_glrlm_LongRunLowGrayLevelEmphasis</th>\n", |
|
|
2907 |
" <td>0.035881</td>\n", |
|
|
2908 |
" <td>0.113548</td>\n", |
|
|
2909 |
" <td>0.012293</td>\n", |
|
|
2910 |
" <td>0.038744</td>\n", |
|
|
2911 |
" <td>0.086753</td>\n", |
|
|
2912 |
" <td>0.022150</td>\n", |
|
|
2913 |
" </tr>\n", |
|
|
2914 |
" <tr>\n", |
|
|
2915 |
" <th>Mstage</th>\n", |
|
|
2916 |
" <td>0.029703</td>\n", |
|
|
2917 |
" <td>0.170613</td>\n", |
|
|
2918 |
" <td>0.000000</td>\n", |
|
|
2919 |
" <td>0.000000</td>\n", |
|
|
2920 |
" <td>0.000000</td>\n", |
|
|
2921 |
" <td>0.000000</td>\n", |
|
|
2922 |
" </tr>\n", |
|
|
2923 |
" <tr>\n", |
|
|
2924 |
" <th>SourceDataset</th>\n", |
|
|
2925 |
" <td>0.158416</td>\n", |
|
|
2926 |
" <td>0.366952</td>\n", |
|
|
2927 |
" <td>0.000000</td>\n", |
|
|
2928 |
" <td>0.836735</td>\n", |
|
|
2929 |
" <td>0.373438</td>\n", |
|
|
2930 |
" <td>0.000000</td>\n", |
|
|
2931 |
" </tr>\n", |
|
|
2932 |
" <tr>\n", |
|
|
2933 |
" <th>Histology_adenocarcinoma</th>\n", |
|
|
2934 |
" <td>0.227723</td>\n", |
|
|
2935 |
" <td>0.421454</td>\n", |
|
|
2936 |
" <td>0.000000</td>\n", |
|
|
2937 |
" <td>0.673469</td>\n", |
|
|
2938 |
" <td>0.473804</td>\n", |
|
|
2939 |
" <td>1.000000</td>\n", |
|
|
2940 |
" </tr>\n", |
|
|
2941 |
" <tr>\n", |
|
|
2942 |
" <th>Histology_large cell</th>\n", |
|
|
2943 |
" <td>0.217822</td>\n", |
|
|
2944 |
" <td>0.414824</td>\n", |
|
|
2945 |
" <td>0.000000</td>\n", |
|
|
2946 |
" <td>0.020408</td>\n", |
|
|
2947 |
" <td>0.142857</td>\n", |
|
|
2948 |
" <td>0.000000</td>\n", |
|
|
2949 |
" </tr>\n", |
|
|
2950 |
" <tr>\n", |
|
|
2951 |
" <th>Histology_nos</th>\n", |
|
|
2952 |
" <td>0.178218</td>\n", |
|
|
2953 |
" <td>0.384605</td>\n", |
|
|
2954 |
" <td>0.000000</td>\n", |
|
|
2955 |
" <td>0.020408</td>\n", |
|
|
2956 |
" <td>0.142857</td>\n", |
|
|
2957 |
" <td>0.000000</td>\n", |
|
|
2958 |
" </tr>\n", |
|
|
2959 |
" <tr>\n", |
|
|
2960 |
" <th>original_glrlm_RunLengthNonUniformity</th>\n", |
|
|
2961 |
" <td>0.110472</td>\n", |
|
|
2962 |
" <td>0.114490</td>\n", |
|
|
2963 |
" <td>0.137792</td>\n", |
|
|
2964 |
" <td>0.059445</td>\n", |
|
|
2965 |
" <td>0.175886</td>\n", |
|
|
2966 |
" <td>0.023126</td>\n", |
|
|
2967 |
" </tr>\n", |
|
|
2968 |
" <tr>\n", |
|
|
2969 |
" <th>original_firstorder_Maximum</th>\n", |
|
|
2970 |
" <td>0.184491</td>\n", |
|
|
2971 |
" <td>0.165187</td>\n", |
|
|
2972 |
" <td>0.165658</td>\n", |
|
|
2973 |
" <td>0.116175</td>\n", |
|
|
2974 |
" <td>0.096689</td>\n", |
|
|
2975 |
" <td>0.063077</td>\n", |
|
|
2976 |
" </tr>\n", |
|
|
2977 |
" <tr>\n", |
|
|
2978 |
" <th>original_glrlm_LongRunHighGrayLevelEmphasis</th>\n", |
|
|
2979 |
" <td>0.205714</td>\n", |
|
|
2980 |
" <td>0.162696</td>\n", |
|
|
2981 |
" <td>0.195596</td>\n", |
|
|
2982 |
" <td>0.066823</td>\n", |
|
|
2983 |
" <td>0.096787</td>\n", |
|
|
2984 |
" <td>0.038876</td>\n", |
|
|
2985 |
" </tr>\n", |
|
|
2986 |
" <tr>\n", |
|
|
2987 |
" <th>original_glrlm_LongRunEmphasis</th>\n", |
|
|
2988 |
" <td>0.146258</td>\n", |
|
|
2989 |
" <td>0.141577</td>\n", |
|
|
2990 |
" <td>0.154030</td>\n", |
|
|
2991 |
" <td>0.036450</td>\n", |
|
|
2992 |
" <td>0.080802</td>\n", |
|
|
2993 |
" <td>0.026298</td>\n", |
|
|
2994 |
" </tr>\n", |
|
|
2995 |
" <tr>\n", |
|
|
2996 |
" <th>original_firstorder_Range</th>\n", |
|
|
2997 |
" <td>0.249277</td>\n", |
|
|
2998 |
" <td>0.156409</td>\n", |
|
|
2999 |
" <td>0.170097</td>\n", |
|
|
3000 |
" <td>0.191130</td>\n", |
|
|
3001 |
" <td>0.101149</td>\n", |
|
|
3002 |
" <td>0.095769</td>\n", |
|
|
3003 |
" </tr>\n", |
|
|
3004 |
" <tr>\n", |
|
|
3005 |
" <th>original_shape_VoxelVolume</th>\n", |
|
|
3006 |
" <td>0.131952</td>\n", |
|
|
3007 |
" <td>0.155133</td>\n", |
|
|
3008 |
" <td>0.154927</td>\n", |
|
|
3009 |
" <td>0.041246</td>\n", |
|
|
3010 |
" <td>0.113124</td>\n", |
|
|
3011 |
" <td>0.014398</td>\n", |
|
|
3012 |
" </tr>\n", |
|
|
3013 |
" <tr>\n", |
|
|
3014 |
" <th>Nstage</th>\n", |
|
|
3015 |
" <td>0.321782</td>\n", |
|
|
3016 |
" <td>0.300701</td>\n", |
|
|
3017 |
" <td>0.500000</td>\n", |
|
|
3018 |
" <td>0.086735</td>\n", |
|
|
3019 |
" <td>0.201240</td>\n", |
|
|
3020 |
" <td>0.000000</td>\n", |
|
|
3021 |
" </tr>\n", |
|
|
3022 |
" <tr>\n", |
|
|
3023 |
" <th>original_glcm_JointEnergy</th>\n", |
|
|
3024 |
" <td>0.166168</td>\n", |
|
|
3025 |
" <td>0.151797</td>\n", |
|
|
3026 |
" <td>0.198795</td>\n", |
|
|
3027 |
" <td>0.047922</td>\n", |
|
|
3028 |
" <td>0.098254</td>\n", |
|
|
3029 |
" <td>0.044295</td>\n", |
|
|
3030 |
" </tr>\n", |
|
|
3031 |
" <tr>\n", |
|
|
3032 |
" <th>original_shape_SurfaceArea</th>\n", |
|
|
3033 |
" <td>0.210168</td>\n", |
|
|
3034 |
" <td>0.183855</td>\n", |
|
|
3035 |
" <td>0.262893</td>\n", |
|
|
3036 |
" <td>0.083413</td>\n", |
|
|
3037 |
" <td>0.170937</td>\n", |
|
|
3038 |
" <td>0.049602</td>\n", |
|
|
3039 |
" </tr>\n", |
|
|
3040 |
" <tr>\n", |
|
|
3041 |
" <th>original_glrlm_GrayLevelNonUniformity</th>\n", |
|
|
3042 |
" <td>0.105473</td>\n", |
|
|
3043 |
" <td>0.156377</td>\n", |
|
|
3044 |
" <td>0.113818</td>\n", |
|
|
3045 |
" <td>0.027657</td>\n", |
|
|
3046 |
" <td>0.089419</td>\n", |
|
|
3047 |
" <td>0.005354</td>\n", |
|
|
3048 |
" </tr>\n", |
|
|
3049 |
" <tr>\n", |
|
|
3050 |
" <th>original_firstorder_Uniformity</th>\n", |
|
|
3051 |
" <td>0.294907</td>\n", |
|
|
3052 |
" <td>0.212134</td>\n", |
|
|
3053 |
" <td>0.331429</td>\n", |
|
|
3054 |
" <td>0.101235</td>\n", |
|
|
3055 |
" <td>0.152075</td>\n", |
|
|
3056 |
" <td>0.091915</td>\n", |
|
|
3057 |
" </tr>\n", |
|
|
3058 |
" <tr>\n", |
|
|
3059 |
" <th>original_firstorder_Kurtosis</th>\n", |
|
|
3060 |
" <td>0.118243</td>\n", |
|
|
3061 |
" <td>0.181936</td>\n", |
|
|
3062 |
" <td>0.126265</td>\n", |
|
|
3063 |
" <td>0.023363</td>\n", |
|
|
3064 |
" <td>0.043704</td>\n", |
|
|
3065 |
" <td>0.012281</td>\n", |
|
|
3066 |
" </tr>\n", |
|
|
3067 |
" <tr>\n", |
|
|
3068 |
" <th>Histology_squamous cell carcinoma</th>\n", |
|
|
3069 |
" <td>0.316832</td>\n", |
|
|
3070 |
" <td>0.467562</td>\n", |
|
|
3071 |
" <td>1.000000</td>\n", |
|
|
3072 |
" <td>0.244898</td>\n", |
|
|
3073 |
" <td>0.434483</td>\n", |
|
|
3074 |
" <td>0.000000</td>\n", |
|
|
3075 |
" </tr>\n", |
|
|
3076 |
" </tbody>\n", |
|
|
3077 |
"</table>\n", |
|
|
3078 |
"</div>" |
|
|
3079 |
], |
|
|
3080 |
"text/plain": [ |
|
|
3081 |
" low_surv_df_mean ... high_surv_Q3Q1_range\n", |
|
|
3082 |
"original_firstorder_Energy 0.061725 ... 0.021991\n", |
|
|
3083 |
"original_firstorder_Median 0.858435 ... 0.463329\n", |
|
|
3084 |
"original_glcm_ClusterShade 0.387256 ... 0.099742\n", |
|
|
3085 |
"original_glcm_Contrast 0.084823 ... 0.264574\n", |
|
|
3086 |
"original_glrlm_LowGrayLevelRunEmphasis 0.039752 ... 0.059311\n", |
|
|
3087 |
"original_glrlm_ShortRunLowGrayLevelEmphasis 0.047246 ... 0.077694\n", |
|
|
3088 |
"original_glrlm_LongRunLowGrayLevelEmphasis 0.035881 ... 0.022150\n", |
|
|
3089 |
"Mstage 0.029703 ... 0.000000\n", |
|
|
3090 |
"SourceDataset 0.158416 ... 0.000000\n", |
|
|
3091 |
"Histology_adenocarcinoma 0.227723 ... 1.000000\n", |
|
|
3092 |
"Histology_large cell 0.217822 ... 0.000000\n", |
|
|
3093 |
"Histology_nos 0.178218 ... 0.000000\n", |
|
|
3094 |
"original_glrlm_RunLengthNonUniformity 0.110472 ... 0.023126\n", |
|
|
3095 |
"original_firstorder_Maximum 0.184491 ... 0.063077\n", |
|
|
3096 |
"original_glrlm_LongRunHighGrayLevelEmphasis 0.205714 ... 0.038876\n", |
|
|
3097 |
"original_glrlm_LongRunEmphasis 0.146258 ... 0.026298\n", |
|
|
3098 |
"original_firstorder_Range 0.249277 ... 0.095769\n", |
|
|
3099 |
"original_shape_VoxelVolume 0.131952 ... 0.014398\n", |
|
|
3100 |
"Nstage 0.321782 ... 0.000000\n", |
|
|
3101 |
"original_glcm_JointEnergy 0.166168 ... 0.044295\n", |
|
|
3102 |
"original_shape_SurfaceArea 0.210168 ... 0.049602\n", |
|
|
3103 |
"original_glrlm_GrayLevelNonUniformity 0.105473 ... 0.005354\n", |
|
|
3104 |
"original_firstorder_Uniformity 0.294907 ... 0.091915\n", |
|
|
3105 |
"original_firstorder_Kurtosis 0.118243 ... 0.012281\n", |
|
|
3106 |
"Histology_squamous cell carcinoma 0.316832 ... 0.000000\n", |
|
|
3107 |
"\n", |
|
|
3108 |
"[25 rows x 6 columns]" |
|
|
3109 |
] |
|
|
3110 |
}, |
|
|
3111 |
"metadata": { |
|
|
3112 |
"tags": [] |
|
|
3113 |
}, |
|
|
3114 |
"execution_count": 25 |
|
|
3115 |
} |
|
|
3116 |
] |
|
|
3117 |
}, |
|
|
3118 |
{ |
|
|
3119 |
"cell_type": "markdown", |
|
|
3120 |
"metadata": { |
|
|
3121 |
"id": "dVDU5HByfiT0", |
|
|
3122 |
"colab_type": "text" |
|
|
3123 |
}, |
|
|
3124 |
"source": [ |
|
|
3125 |
"**Conclusion**: The discriminating feature are `SourceDataset`, `Histology_adenocarcinoma`. " |
|
|
3126 |
] |
|
|
3127 |
}, |
|
|
3128 |
{ |
|
|
3129 |
"cell_type": "markdown", |
|
|
3130 |
"metadata": { |
|
|
3131 |
"id": "FKMH1AKGP8VB", |
|
|
3132 |
"colab_type": "text" |
|
|
3133 |
}, |
|
|
3134 |
"source": [ |
|
|
3135 |
"### Classification of patients to determine low / high survival probability" |
|
|
3136 |
] |
|
|
3137 |
}, |
|
|
3138 |
{ |
|
|
3139 |
"cell_type": "code", |
|
|
3140 |
"metadata": { |
|
|
3141 |
"id": "EaKY-1MG16V7", |
|
|
3142 |
"colab_type": "code", |
|
|
3143 |
"outputId": "fed18228-80df-4bee-be20-7c8d2d7d027f", |
|
|
3144 |
"colab": { |
|
|
3145 |
"base_uri": "https://localhost:8080/", |
|
|
3146 |
"height": 340 |
|
|
3147 |
} |
|
|
3148 |
}, |
|
|
3149 |
"source": [ |
|
|
3150 |
"!pip install scikit-survival\n", |
|
|
3151 |
"from sksurv.ensemble import RandomSurvivalForest\n", |
|
|
3152 |
"from sklearn.model_selection import cross_validate, RandomizedSearchCV" |
|
|
3153 |
], |
|
|
3154 |
"execution_count": 0, |
|
|
3155 |
"outputs": [ |
|
|
3156 |
{ |
|
|
3157 |
"output_type": "stream", |
|
|
3158 |
"text": [ |
|
|
3159 |
"Requirement already satisfied: scikit-survival in /usr/local/lib/python3.6/dist-packages (0.11)\n", |
|
|
3160 |
"Requirement already satisfied: scikit-learn<0.22,>=0.21.0 in /usr/local/lib/python3.6/dist-packages (from scikit-survival) (0.21.3)\n", |
|
|
3161 |
"Requirement already satisfied: numexpr in /usr/local/lib/python3.6/dist-packages (from scikit-survival) (2.7.1)\n", |
|
|
3162 |
"Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from scikit-survival) (1.18.1)\n", |
|
|
3163 |
"Requirement already satisfied: osqp!=0.6.0,!=0.6.1 in /usr/local/lib/python3.6/dist-packages (from scikit-survival) (0.5.0)\n", |
|
|
3164 |
"Requirement already satisfied: cython>=0.29 in /usr/local/lib/python3.6/dist-packages (from scikit-survival) (0.29.15)\n", |
|
|
3165 |
"Requirement already satisfied: cvxpy>=1.0 in /usr/local/lib/python3.6/dist-packages (from scikit-survival) (1.0.28)\n", |
|
|
3166 |
"Requirement already satisfied: scipy!=1.3.0,>=1.0 in /usr/local/lib/python3.6/dist-packages (from scikit-survival) (1.4.1)\n", |
|
|
3167 |
"Requirement already satisfied: pandas<0.26,>=0.21 in /usr/local/lib/python3.6/dist-packages (from scikit-survival) (0.25.3)\n", |
|
|
3168 |
"Requirement already satisfied: cvxopt in /usr/local/lib/python3.6/dist-packages (from scikit-survival) (1.2.4)\n", |
|
|
3169 |
"Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.6/dist-packages (from scikit-learn<0.22,>=0.21.0->scikit-survival) (0.14.1)\n", |
|
|
3170 |
"Requirement already satisfied: future in /usr/local/lib/python3.6/dist-packages (from osqp!=0.6.0,!=0.6.1->scikit-survival) (0.16.0)\n", |
|
|
3171 |
"Requirement already satisfied: multiprocess in /usr/local/lib/python3.6/dist-packages (from cvxpy>=1.0->scikit-survival) (0.70.9)\n", |
|
|
3172 |
"Requirement already satisfied: scs>=1.1.3 in /usr/local/lib/python3.6/dist-packages (from cvxpy>=1.0->scikit-survival) (2.1.1.post2)\n", |
|
|
3173 |
"Requirement already satisfied: ecos>=2 in /usr/local/lib/python3.6/dist-packages (from cvxpy>=1.0->scikit-survival) (2.0.7.post1)\n", |
|
|
3174 |
"Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.6/dist-packages (from pandas<0.26,>=0.21->scikit-survival) (2018.9)\n", |
|
|
3175 |
"Requirement already satisfied: python-dateutil>=2.6.1 in /usr/local/lib/python3.6/dist-packages (from pandas<0.26,>=0.21->scikit-survival) (2.8.1)\n", |
|
|
3176 |
"Requirement already satisfied: dill>=0.3.1 in /usr/local/lib/python3.6/dist-packages (from multiprocess->cvxpy>=1.0->scikit-survival) (0.3.1.1)\n", |
|
|
3177 |
"Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.6/dist-packages (from python-dateutil>=2.6.1->pandas<0.26,>=0.21->scikit-survival) (1.12.0)\n" |
|
|
3178 |
], |
|
|
3179 |
"name": "stdout" |
|
|
3180 |
} |
|
|
3181 |
] |
|
|
3182 |
}, |
|
|
3183 |
{ |
|
|
3184 |
"cell_type": "code", |
|
|
3185 |
"metadata": { |
|
|
3186 |
"id": "lQFcod5yQQts", |
|
|
3187 |
"colab_type": "code", |
|
|
3188 |
"colab": {} |
|
|
3189 |
}, |
|
|
3190 |
"source": [ |
|
|
3191 |
"\"\"\" Concatenate the values of both population and set labels for classification \"\"\"\n", |
|
|
3192 |
"x_surv = low_surv_df.append(high_surv_df)\n", |
|
|
3193 |
"y_low_surv = pd.Series([0] * len(low_surv_df), index=low_surv_df.index, name='high_surv')\n", |
|
|
3194 |
"y_high_surv = pd.Series([1] * len(high_surv_df), index=high_survival_index, name='high_surv')\n", |
|
|
3195 |
"y_surv = y_low_surv.append(y_high_surv)" |
|
|
3196 |
], |
|
|
3197 |
"execution_count": 0, |
|
|
3198 |
"outputs": [] |
|
|
3199 |
}, |
|
|
3200 |
{ |
|
|
3201 |
"cell_type": "code", |
|
|
3202 |
"metadata": { |
|
|
3203 |
"id": "OAeMgc6yaHpK", |
|
|
3204 |
"colab_type": "code", |
|
|
3205 |
"outputId": "6a689f09-d6c5-4d69-b393-464c57e48d74", |
|
|
3206 |
"colab": { |
|
|
3207 |
"base_uri": "https://localhost:8080/", |
|
|
3208 |
"height": 564 |
|
|
3209 |
} |
|
|
3210 |
}, |
|
|
3211 |
"source": [ |
|
|
3212 |
"from sklearn.ensemble import RandomForestClassifier\n", |
|
|
3213 |
"\n", |
|
|
3214 |
"tuned_params = {\"n_estimators\": np.arange(10, 1000, 10),\n", |
|
|
3215 |
" \"min_samples_split\": np.arange(2, 40, 2),\n", |
|
|
3216 |
" \"min_samples_leaf\": np.arange(1, 20, 1)\n", |
|
|
3217 |
" }\n", |
|
|
3218 |
"\n", |
|
|
3219 |
"search = RandomizedSearchCV(RandomForestClassifier(), tuned_params, cv=5, verbose=2, n_jobs=-1, n_iter=100)\n", |
|
|
3220 |
"search.fit(x_surv, y_surv)" |
|
|
3221 |
], |
|
|
3222 |
"execution_count": 0, |
|
|
3223 |
"outputs": [ |
|
|
3224 |
{ |
|
|
3225 |
"output_type": "stream", |
|
|
3226 |
"text": [ |
|
|
3227 |
"Fitting 5 folds for each of 100 candidates, totalling 500 fits\n" |
|
|
3228 |
], |
|
|
3229 |
"name": "stdout" |
|
|
3230 |
}, |
|
|
3231 |
{ |
|
|
3232 |
"output_type": "stream", |
|
|
3233 |
"text": [ |
|
|
3234 |
"[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n", |
|
|
3235 |
"[Parallel(n_jobs=-1)]: Done 37 tasks | elapsed: 23.4s\n", |
|
|
3236 |
"[Parallel(n_jobs=-1)]: Done 158 tasks | elapsed: 1.6min\n", |
|
|
3237 |
"[Parallel(n_jobs=-1)]: Done 361 tasks | elapsed: 3.4min\n", |
|
|
3238 |
"[Parallel(n_jobs=-1)]: Done 500 out of 500 | elapsed: 4.7min finished\n", |
|
|
3239 |
"/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_search.py:814: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n", |
|
|
3240 |
" DeprecationWarning)\n" |
|
|
3241 |
], |
|
|
3242 |
"name": "stderr" |
|
|
3243 |
}, |
|
|
3244 |
{ |
|
|
3245 |
"output_type": "execute_result", |
|
|
3246 |
"data": { |
|
|
3247 |
"text/plain": [ |
|
|
3248 |
"RandomizedSearchCV(cv=5, error_score='raise-deprecating',\n", |
|
|
3249 |
" estimator=RandomForestClassifier(bootstrap=True,\n", |
|
|
3250 |
" class_weight=None,\n", |
|
|
3251 |
" criterion='gini',\n", |
|
|
3252 |
" max_depth=None,\n", |
|
|
3253 |
" max_features='auto',\n", |
|
|
3254 |
" max_leaf_nodes=None,\n", |
|
|
3255 |
" min_impurity_decrease=0.0,\n", |
|
|
3256 |
" min_impurity_split=None,\n", |
|
|
3257 |
" min_samples_leaf=1,\n", |
|
|
3258 |
" min_samples_split=2,\n", |
|
|
3259 |
" min_weight_fraction_leaf=0.0,\n", |
|
|
3260 |
" n_estimators='warn',\n", |
|
|
3261 |
" n_jobs=None,\n", |
|
|
3262 |
" oob_sc...\n", |
|
|
3263 |
" 270, 280, 290, 300, 310, 320, 330, 340, 350, 360, 370, 380, 390,\n", |
|
|
3264 |
" 400, 410, 420, 430, 440, 450, 460, 470, 480, 490, 500, 510, 520,\n", |
|
|
3265 |
" 530, 540, 550, 560, 570, 580, 590, 600, 610, 620, 630, 640, 650,\n", |
|
|
3266 |
" 660, 670, 680, 690, 700, 710, 720, 730, 740, 750, 760, 770, 780,\n", |
|
|
3267 |
" 790, 800, 810, 820, 830, 840, 850, 860, 870, 880, 890, 900, 910,\n", |
|
|
3268 |
" 920, 930, 940, 950, 960, 970, 980, 990])},\n", |
|
|
3269 |
" pre_dispatch='2*n_jobs', random_state=None, refit=True,\n", |
|
|
3270 |
" return_train_score=False, scoring=None, verbose=2)" |
|
|
3271 |
] |
|
|
3272 |
}, |
|
|
3273 |
"metadata": { |
|
|
3274 |
"tags": [] |
|
|
3275 |
}, |
|
|
3276 |
"execution_count": 29 |
|
|
3277 |
} |
|
|
3278 |
] |
|
|
3279 |
}, |
|
|
3280 |
{ |
|
|
3281 |
"cell_type": "code", |
|
|
3282 |
"metadata": { |
|
|
3283 |
"id": "nqFyb4-R1r5h", |
|
|
3284 |
"colab_type": "code", |
|
|
3285 |
"outputId": "9f2114b5-e124-4338-e895-11edefa90879", |
|
|
3286 |
"colab": { |
|
|
3287 |
"base_uri": "https://localhost:8080/", |
|
|
3288 |
"height": 68 |
|
|
3289 |
} |
|
|
3290 |
}, |
|
|
3291 |
"source": [ |
|
|
3292 |
"params = search.best_params_\n", |
|
|
3293 |
"forest = RandomForestClassifier(**params, n_jobs=-1)\n", |
|
|
3294 |
"cross_validate(forest, x_surv, y_surv, cv=5)" |
|
|
3295 |
], |
|
|
3296 |
"execution_count": 0, |
|
|
3297 |
"outputs": [ |
|
|
3298 |
{ |
|
|
3299 |
"output_type": "execute_result", |
|
|
3300 |
"data": { |
|
|
3301 |
"text/plain": [ |
|
|
3302 |
"{'fit_time': array([1.40461445, 1.35777259, 1.36289883, 1.35091567, 1.38806367]),\n", |
|
|
3303 |
" 'score_time': array([0.20353508, 0.20422983, 0.20304966, 0.20344043, 0.20493698]),\n", |
|
|
3304 |
" 'test_score': array([0.77419355, 0.86666667, 0.83333333, 0.66666667, 0.86206897])}" |
|
|
3305 |
] |
|
|
3306 |
}, |
|
|
3307 |
"metadata": { |
|
|
3308 |
"tags": [] |
|
|
3309 |
}, |
|
|
3310 |
"execution_count": 30 |
|
|
3311 |
} |
|
|
3312 |
] |
|
|
3313 |
}, |
|
|
3314 |
{ |
|
|
3315 |
"cell_type": "code", |
|
|
3316 |
"metadata": { |
|
|
3317 |
"id": "qa1TUMUpeNuu", |
|
|
3318 |
"colab_type": "code", |
|
|
3319 |
"outputId": "a7839865-818c-4dfb-9b85-cf833fdfe75c", |
|
|
3320 |
"colab": { |
|
|
3321 |
"base_uri": "https://localhost:8080/", |
|
|
3322 |
"height": 1000 |
|
|
3323 |
} |
|
|
3324 |
}, |
|
|
3325 |
"source": [ |
|
|
3326 |
"forest = RandomForestClassifier(**params, n_jobs=-1)\n", |
|
|
3327 |
"forest.fit(x_surv, y_surv)\n", |
|
|
3328 |
"\n", |
|
|
3329 |
"\"\"\" Feature importance \"\"\"\n", |
|
|
3330 |
"importances = forest.feature_importances_\n", |
|
|
3331 |
"std = np.std([forest.feature_importances_ for forest in forest.estimators_],\n", |
|
|
3332 |
" axis=0)\n", |
|
|
3333 |
"indices = np.argsort(importances)[::-1]\n", |
|
|
3334 |
"\n", |
|
|
3335 |
"# Print the feature ranking\n", |
|
|
3336 |
"print(\"Feature ranking:\")\n", |
|
|
3337 |
"\n", |
|
|
3338 |
"for f in range(x_surv.shape[1]):\n", |
|
|
3339 |
" print('{:<60}({:<10})+/-({:})'.format(x_surv.columns[indices[f]], importances[indices[f]], std[indices[f]]))" |
|
|
3340 |
], |
|
|
3341 |
"execution_count": 0, |
|
|
3342 |
"outputs": [ |
|
|
3343 |
{ |
|
|
3344 |
"output_type": "stream", |
|
|
3345 |
"text": [ |
|
|
3346 |
"Feature ranking:\n", |
|
|
3347 |
"SourceDataset (0.07928941507342958)+/-(0.1738562857046239)\n", |
|
|
3348 |
"original_glcm_Idmn (0.05000366757650152)+/-(0.1326527696599052)\n", |
|
|
3349 |
"original_glrlm_LongRunEmphasis (0.04766199759860742)+/-(0.13588000111211174)\n", |
|
|
3350 |
"original_glrlm_RunPercentage (0.0413548499906998)+/-(0.1285057672776681)\n", |
|
|
3351 |
"original_glcm_DifferenceEntropy (0.03767301771706796)+/-(0.11102341757585656)\n", |
|
|
3352 |
"original_glcm_Idm (0.035154865505996155)+/-(0.11498423941592535)\n", |
|
|
3353 |
"original_glcm_Imc1 (0.03382182182491566)+/-(0.08791147430504223)\n", |
|
|
3354 |
"original_glcm_Id (0.03378343876684833)+/-(0.11102977580858837)\n", |
|
|
3355 |
"original_glrlm_LongRunHighGrayLevelEmphasis (0.03338372843681382)+/-(0.10692969988476542)\n", |
|
|
3356 |
"original_glrlm_ShortRunEmphasis (0.033287283756406195)+/-(0.10893932707538749)\n", |
|
|
3357 |
"original_glcm_Idn (0.03155443996849472)+/-(0.10782824975641177)\n", |
|
|
3358 |
"original_glcm_JointEnergy (0.02607598817710038)+/-(0.09166921247445962)\n", |
|
|
3359 |
"original_glcm_DifferenceAverage (0.025521933365931483)+/-(0.08554694697078281)\n", |
|
|
3360 |
"original_glcm_Contrast (0.025390737199290768)+/-(0.0874579950881286)\n", |
|
|
3361 |
"original_glcm_Correlation (0.022857083876227226)+/-(0.07176656177988945)\n", |
|
|
3362 |
"original_shape_SurfaceVolumeRatio (0.01868494201214343)+/-(0.06938865355604999)\n", |
|
|
3363 |
"original_glcm_MaximumProbability (0.017946106206776922)+/-(0.06162369864321631)\n", |
|
|
3364 |
"original_glrlm_GrayLevelNonUniformity (0.017058387767660776)+/-(0.05565886630735466)\n", |
|
|
3365 |
"original_firstorder_Uniformity (0.016636411236540874)+/-(0.06137360288886845)\n", |
|
|
3366 |
"original_glcm_Imc2 (0.016212048233630337)+/-(0.03670560507271377)\n", |
|
|
3367 |
"original_firstorder_Mean (0.01615072519325507)+/-(0.05641177530946689)\n", |
|
|
3368 |
"age (0.015236974917444291)+/-(0.03268252184607131)\n", |
|
|
3369 |
"original_firstorder_Kurtosis (0.01500150735000768)+/-(0.058834397988317876)\n", |
|
|
3370 |
"original_glcm_JointEntropy (0.014674722164697722)+/-(0.06170821320180433)\n", |
|
|
3371 |
"original_glcm_InverseVariance (0.014468042139639644)+/-(0.06043251106208118)\n", |
|
|
3372 |
"original_firstorder_RootMeanSquared (0.011876901868422399)+/-(0.0443656375752213)\n", |
|
|
3373 |
"original_glcm_ClusterShade (0.011768631517728955)+/-(0.03210270989461498)\n", |
|
|
3374 |
"original_shape_Maximum3DDiameter (0.011280393039739437)+/-(0.03909710632328299)\n", |
|
|
3375 |
"original_shape_VoxelVolume (0.0110971298226257)+/-(0.04950917261301518)\n", |
|
|
3376 |
"original_firstorder_Entropy (0.010758596040198598)+/-(0.039801034216223566)\n", |
|
|
3377 |
"original_shape_Compactness1 (0.010517396224149189)+/-(0.02748864605282961)\n", |
|
|
3378 |
"original_firstorder_Range (0.010391871361414718)+/-(0.029027476868529547)\n", |
|
|
3379 |
"original_shape_SphericalDisproportion (0.010193560902043791)+/-(0.027280161944424692)\n", |
|
|
3380 |
"original_glrlm_LongRunLowGrayLevelEmphasis (0.010064286225441239)+/-(0.026483147295626633)\n", |
|
|
3381 |
"original_glcm_SumEntropy (0.009930883280899297)+/-(0.03429463032595291)\n", |
|
|
3382 |
"original_shape_Compactness2 (0.009916953407792568)+/-(0.02669903728388564)\n", |
|
|
3383 |
"original_firstorder_Median (0.009826169550080272)+/-(0.038513944014570055)\n", |
|
|
3384 |
"original_firstorder_Skewness (0.009646341454065415)+/-(0.03176341593485137)\n", |
|
|
3385 |
"original_glrlm_RunLengthNonUniformity (0.00938966406285514)+/-(0.03427036696151096)\n", |
|
|
3386 |
"original_glcm_ClusterTendency (0.009291111457825262)+/-(0.028062783591381243)\n", |
|
|
3387 |
"original_glrlm_LowGrayLevelRunEmphasis (0.00916446281570515)+/-(0.028451843567701297)\n", |
|
|
3388 |
"original_glrlm_ShortRunLowGrayLevelEmphasis (0.008893859357832242)+/-(0.03688150039797808)\n", |
|
|
3389 |
"original_glrlm_ShortRunHighGrayLevelEmphasis (0.00874946722879777)+/-(0.025557815888771776)\n", |
|
|
3390 |
"original_firstorder_Energy (0.00835200836358638)+/-(0.02575526613322083)\n", |
|
|
3391 |
"original_shape_Sphericity (0.007976238798976592)+/-(0.02370123126677445)\n", |
|
|
3392 |
"original_firstorder_MeanAbsoluteDeviation (0.0077194592288307)+/-(0.025002643351615228)\n", |
|
|
3393 |
"original_shape_SurfaceArea (0.00761925849062389)+/-(0.0277261559796855)\n", |
|
|
3394 |
"original_glcm_ClusterProminence (0.0075163172658246095)+/-(0.023806276171445158)\n", |
|
|
3395 |
"original_firstorder_StandardDeviation (0.007380895850395991)+/-(0.024291787304540225)\n", |
|
|
3396 |
"original_firstorder_Variance (0.0073034446738674715)+/-(0.023403898588759985)\n", |
|
|
3397 |
"Nstage (0.007121281067232745)+/-(0.029991614598979203)\n", |
|
|
3398 |
"original_firstorder_Maximum (0.006955378590824373)+/-(0.021323361549575336)\n", |
|
|
3399 |
"original_glrlm_HighGrayLevelRunEmphasis (0.006452130326901698)+/-(0.02136312515839412)\n", |
|
|
3400 |
"original_glcm_SumAverage (0.005629607880727473)+/-(0.020019128035455618)\n", |
|
|
3401 |
"original_firstorder_Minimum (0.005584798315219134)+/-(0.01829629427309807)\n", |
|
|
3402 |
"original_glcm_Autocorrelation (0.0052110079737306)+/-(0.018649539177845665)\n", |
|
|
3403 |
"Histology_adenocarcinoma (0.004009105824542984)+/-(0.022113055882791853)\n", |
|
|
3404 |
"Tstage (0.002082367686795266)+/-(0.010675166792902913)\n", |
|
|
3405 |
"Histology_squamous cell carcinoma (0.0010342795272859074)+/-(0.007327884470672186)\n", |
|
|
3406 |
"Histology_large cell (0.00023143569230502548)+/-(0.0034266607668973856)\n", |
|
|
3407 |
"Histology_nos (0.00017916876858443723)+/-(0.003414966515017873)\n", |
|
|
3408 |
"Mstage (0.0 )+/-(0.0)\n" |
|
|
3409 |
], |
|
|
3410 |
"name": "stdout" |
|
|
3411 |
} |
|
|
3412 |
] |
|
|
3413 |
}, |
|
|
3414 |
{ |
|
|
3415 |
"cell_type": "code", |
|
|
3416 |
"metadata": { |
|
|
3417 |
"id": "Nd_hSCaZmNdV", |
|
|
3418 |
"colab_type": "code", |
|
|
3419 |
"colab": {} |
|
|
3420 |
}, |
|
|
3421 |
"source": [ |
|
|
3422 |
"high_importance_features_save = ['SourceDataset',\n", |
|
|
3423 |
" 'original_glcm_Idn',\n", |
|
|
3424 |
" 'original_glcm_Idmn',\n", |
|
|
3425 |
" 'original_glcm_Imc1',\n", |
|
|
3426 |
" 'original_glcm_Idm',\n", |
|
|
3427 |
" 'original_glrlm_RunPercentage',\n", |
|
|
3428 |
" 'original_glrlm_LongRunEmphasis',\n", |
|
|
3429 |
" 'original_glcm_Id',\n", |
|
|
3430 |
" 'original_glcm_DifferenceAverage',\n", |
|
|
3431 |
" 'original_glrlm_LongRunHighGrayLevelEmphasis']\n", |
|
|
3432 |
"\n", |
|
|
3433 |
"# high_importance_features = [x_surv.columns[indices[f]] for f in range(10)]\n", |
|
|
3434 |
"# high_importance_features" |
|
|
3435 |
], |
|
|
3436 |
"execution_count": 0, |
|
|
3437 |
"outputs": [] |
|
|
3438 |
}, |
|
|
3439 |
{ |
|
|
3440 |
"cell_type": "markdown", |
|
|
3441 |
"metadata": { |
|
|
3442 |
"id": "M6EAi_tWV_HK", |
|
|
3443 |
"colab_type": "text" |
|
|
3444 |
}, |
|
|
3445 |
"source": [ |
|
|
3446 |
"## Covariance matrix\n", |
|
|
3447 |
"\n", |
|
|
3448 |
"**Question**: how to exploit correlation matrix to eliminate non informative features?" |
|
|
3449 |
] |
|
|
3450 |
}, |
|
|
3451 |
{ |
|
|
3452 |
"cell_type": "markdown", |
|
|
3453 |
"metadata": { |
|
|
3454 |
"id": "M7qvAUp5TMJb", |
|
|
3455 |
"colab_type": "text" |
|
|
3456 |
}, |
|
|
3457 |
"source": [ |
|
|
3458 |
"https://towardsdatascience.com/why-feature-correlation-matters-a-lot-847e8ba439c4" |
|
|
3459 |
] |
|
|
3460 |
}, |
|
|
3461 |
{ |
|
|
3462 |
"cell_type": "code", |
|
|
3463 |
"metadata": { |
|
|
3464 |
"id": "pi_3RrmMF6kG", |
|
|
3465 |
"colab_type": "code", |
|
|
3466 |
"colab": {} |
|
|
3467 |
}, |
|
|
3468 |
"source": [ |
|
|
3469 |
"import seaborn as sn" |
|
|
3470 |
], |
|
|
3471 |
"execution_count": 0, |
|
|
3472 |
"outputs": [] |
|
|
3473 |
}, |
|
|
3474 |
{ |
|
|
3475 |
"cell_type": "code", |
|
|
3476 |
"metadata": { |
|
|
3477 |
"id": "ytSjjlQHWBC7", |
|
|
3478 |
"colab_type": "code", |
|
|
3479 |
"colab": {} |
|
|
3480 |
}, |
|
|
3481 |
"source": [ |
|
|
3482 |
"input_train, output_train, input_test = load_owkin_data()" |
|
|
3483 |
], |
|
|
3484 |
"execution_count": 0, |
|
|
3485 |
"outputs": [] |
|
|
3486 |
}, |
|
|
3487 |
{ |
|
|
3488 |
"cell_type": "code", |
|
|
3489 |
"metadata": { |
|
|
3490 |
"id": "M7IB1ZTtFePP", |
|
|
3491 |
"colab_type": "code", |
|
|
3492 |
"colab": {} |
|
|
3493 |
}, |
|
|
3494 |
"source": [ |
|
|
3495 |
"\"\"\" Correlation matrix \"\"\"\n", |
|
|
3496 |
"plt.figure(figsize=(15,15))\n", |
|
|
3497 |
"corrMatrix = input_train.corr(method='pearson')\n", |
|
|
3498 |
"sn.heatmap(corrMatrix)" |
|
|
3499 |
], |
|
|
3500 |
"execution_count": 0, |
|
|
3501 |
"outputs": [] |
|
|
3502 |
}, |
|
|
3503 |
{ |
|
|
3504 |
"cell_type": "code", |
|
|
3505 |
"metadata": { |
|
|
3506 |
"id": "6XqqnTd3L7Bt", |
|
|
3507 |
"colab_type": "code", |
|
|
3508 |
"colab": {} |
|
|
3509 |
}, |
|
|
3510 |
"source": [ |
|
|
3511 |
"\"\"\" Correlation matrix \"\"\"\n", |
|
|
3512 |
"plt.figure(figsize=(15,15))\n", |
|
|
3513 |
"corrMatrix = input_train.corr(method='spearman')\n", |
|
|
3514 |
"sn.heatmap(corrMatrix)" |
|
|
3515 |
], |
|
|
3516 |
"execution_count": 0, |
|
|
3517 |
"outputs": [] |
|
|
3518 |
}, |
|
|
3519 |
{ |
|
|
3520 |
"cell_type": "code", |
|
|
3521 |
"metadata": { |
|
|
3522 |
"id": "MF1XG7YJHAPm", |
|
|
3523 |
"colab_type": "code", |
|
|
3524 |
"colab": {} |
|
|
3525 |
}, |
|
|
3526 |
"source": [ |
|
|
3527 |
"start_features = ['SourceDataset', 'Histology_adenocarcinoma']" |
|
|
3528 |
], |
|
|
3529 |
"execution_count": 0, |
|
|
3530 |
"outputs": [] |
|
|
3531 |
}, |
|
|
3532 |
{ |
|
|
3533 |
"cell_type": "markdown", |
|
|
3534 |
"metadata": { |
|
|
3535 |
"id": "o-rCb6m1dv7G", |
|
|
3536 |
"colab_type": "text" |
|
|
3537 |
}, |
|
|
3538 |
"source": [ |
|
|
3539 |
"# Wavelet features" |
|
|
3540 |
] |
|
|
3541 |
}, |
|
|
3542 |
{ |
|
|
3543 |
"cell_type": "code", |
|
|
3544 |
"metadata": { |
|
|
3545 |
"id": "2F9nSJCKeWdT", |
|
|
3546 |
"colab_type": "code", |
|
|
3547 |
"outputId": "fde61cc0-c61c-42fc-a4d0-a1d074566d9c", |
|
|
3548 |
"colab": { |
|
|
3549 |
"base_uri": "https://localhost:8080/", |
|
|
3550 |
"height": 411 |
|
|
3551 |
} |
|
|
3552 |
}, |
|
|
3553 |
"source": [ |
|
|
3554 |
"!pip install SimpleITK\n", |
|
|
3555 |
"!pip install pyradiomics" |
|
|
3556 |
], |
|
|
3557 |
"execution_count": 0, |
|
|
3558 |
"outputs": [ |
|
|
3559 |
{ |
|
|
3560 |
"output_type": "stream", |
|
|
3561 |
"text": [ |
|
|
3562 |
"Requirement already satisfied: SimpleITK in /usr/local/lib/python3.6/dist-packages (1.2.4)\n", |
|
|
3563 |
"Collecting pyradiomics\n", |
|
|
3564 |
"\u001b[?25l Downloading https://files.pythonhosted.org/packages/73/f1/39b0f758735f920bca7afb4e1edde83a98e2c56432d3e6560abf1a09c091/pyradiomics-3.0-cp36-cp36m-manylinux1_x86_64.whl (157kB)\n", |
|
|
3565 |
"\u001b[K |████████████████████████████████| 163kB 4.8MB/s \n", |
|
|
3566 |
"\u001b[?25hCollecting PyWavelets<=1.0.0,>=0.4.0\n", |
|
|
3567 |
"\u001b[?25l Downloading https://files.pythonhosted.org/packages/90/d0/09b2bf3368d5bba6ee1a8868ce94eebbb105fc8bf89fa43c90348b21a7cb/PyWavelets-1.0.0-cp36-cp36m-manylinux1_x86_64.whl (4.4MB)\n", |
|
|
3568 |
"\u001b[K |████████████████████████████████| 4.4MB 14.7MB/s \n", |
|
|
3569 |
"\u001b[?25hRequirement already satisfied: SimpleITK>=0.9.1 in /usr/local/lib/python3.6/dist-packages (from pyradiomics) (1.2.4)\n", |
|
|
3570 |
"Requirement already satisfied: numpy>=1.9.2 in /usr/local/lib/python3.6/dist-packages (from pyradiomics) (1.18.1)\n", |
|
|
3571 |
"Collecting pykwalify>=1.6.0\n", |
|
|
3572 |
"\u001b[?25l Downloading https://files.pythonhosted.org/packages/36/9f/612de8ca540bd24d604f544248c4c46e9db76f6ea5eb75fb4244da6ebbf0/pykwalify-1.7.0-py2.py3-none-any.whl (40kB)\n", |
|
|
3573 |
"\u001b[K |████████████████████████████████| 40kB 3.4MB/s \n", |
|
|
3574 |
"\u001b[?25hRequirement already satisfied: six>=1.10.0 in /usr/local/lib/python3.6/dist-packages (from pyradiomics) (1.12.0)\n", |
|
|
3575 |
"Requirement already satisfied: docopt>=0.6.2 in /usr/local/lib/python3.6/dist-packages (from pykwalify>=1.6.0->pyradiomics) (0.6.2)\n", |
|
|
3576 |
"Requirement already satisfied: PyYAML>=3.11 in /usr/local/lib/python3.6/dist-packages (from pykwalify>=1.6.0->pyradiomics) (3.13)\n", |
|
|
3577 |
"Requirement already satisfied: python-dateutil>=2.4.2 in /usr/local/lib/python3.6/dist-packages (from pykwalify>=1.6.0->pyradiomics) (2.8.1)\n", |
|
|
3578 |
"\u001b[31mERROR: albumentations 0.1.12 has requirement imgaug<0.2.7,>=0.2.5, but you'll have imgaug 0.2.9 which is incompatible.\u001b[0m\n", |
|
|
3579 |
"Installing collected packages: PyWavelets, pykwalify, pyradiomics\n", |
|
|
3580 |
" Found existing installation: PyWavelets 1.1.1\n", |
|
|
3581 |
" Uninstalling PyWavelets-1.1.1:\n", |
|
|
3582 |
" Successfully uninstalled PyWavelets-1.1.1\n", |
|
|
3583 |
"Successfully installed PyWavelets-1.0.0 pykwalify-1.7.0 pyradiomics-3.0\n" |
|
|
3584 |
], |
|
|
3585 |
"name": "stdout" |
|
|
3586 |
} |
|
|
3587 |
] |
|
|
3588 |
}, |
|
|
3589 |
{ |
|
|
3590 |
"cell_type": "code", |
|
|
3591 |
"metadata": { |
|
|
3592 |
"id": "EJZV5Ynfd3kc", |
|
|
3593 |
"colab_type": "code", |
|
|
3594 |
"colab": {} |
|
|
3595 |
}, |
|
|
3596 |
"source": [ |
|
|
3597 |
"# Open one image\n", |
|
|
3598 |
"import numpy as np\n", |
|
|
3599 |
"archive = np.load(\"data/train/images/patient_002.npz\")\n", |
|
|
3600 |
"scan = archive['scan']\n", |
|
|
3601 |
"mask = archive['mask']" |
|
|
3602 |
], |
|
|
3603 |
"execution_count": 0, |
|
|
3604 |
"outputs": [] |
|
|
3605 |
}, |
|
|
3606 |
{ |
|
|
3607 |
"cell_type": "code", |
|
|
3608 |
"metadata": { |
|
|
3609 |
"id": "7qXYK_TWeTO5", |
|
|
3610 |
"colab_type": "code", |
|
|
3611 |
"colab": {} |
|
|
3612 |
}, |
|
|
3613 |
"source": [ |
|
|
3614 |
"# Convert to SimpleITK format data\n", |
|
|
3615 |
"import SimpleITK as sitk\n", |
|
|
3616 |
"scanimg = sitk.GetImageFromArray(scan)\n", |
|
|
3617 |
"sitk.WriteImage(scanimg, \"data/train/images/patient_002_sikt_scan.nrrd\") \n", |
|
|
3618 |
"scanmask = sitk.GetImageFromArray(np.array(mask,dtype=int))\n", |
|
|
3619 |
"sitk.WriteImage(scanmask, \"data/train/images/patient_002_sikt_mask.nrrd\") " |
|
|
3620 |
], |
|
|
3621 |
"execution_count": 0, |
|
|
3622 |
"outputs": [] |
|
|
3623 |
}, |
|
|
3624 |
{ |
|
|
3625 |
"cell_type": "code", |
|
|
3626 |
"metadata": { |
|
|
3627 |
"id": "eW6JukZ8gM2K", |
|
|
3628 |
"colab_type": "code", |
|
|
3629 |
"outputId": "9f2784b4-0693-4814-9bae-ac92ded91937", |
|
|
3630 |
"colab": { |
|
|
3631 |
"base_uri": "https://localhost:8080/", |
|
|
3632 |
"height": 34 |
|
|
3633 |
} |
|
|
3634 |
}, |
|
|
3635 |
"source": [ |
|
|
3636 |
"# Radiomics Feature Extractor\n", |
|
|
3637 |
"from radiomics import featureextractor\n", |
|
|
3638 |
"extractor = featureextractor.RadiomicsFeatureExtractor()\n", |
|
|
3639 |
"print(extractor.featureClassNames)" |
|
|
3640 |
], |
|
|
3641 |
"execution_count": 0, |
|
|
3642 |
"outputs": [ |
|
|
3643 |
{ |
|
|
3644 |
"output_type": "stream", |
|
|
3645 |
"text": [ |
|
|
3646 |
"['firstorder', 'glcm', 'gldm', 'glrlm', 'glszm', 'ngtdm', 'shape', 'shape2D']\n" |
|
|
3647 |
], |
|
|
3648 |
"name": "stdout" |
|
|
3649 |
} |
|
|
3650 |
] |
|
|
3651 |
}, |
|
|
3652 |
{ |
|
|
3653 |
"cell_type": "code", |
|
|
3654 |
"metadata": { |
|
|
3655 |
"id": "gMUJAhoTe4AS", |
|
|
3656 |
"colab_type": "code", |
|
|
3657 |
"outputId": "b11ceb9d-b4d7-49bb-90b7-f05083ac3daa", |
|
|
3658 |
"colab": { |
|
|
3659 |
"base_uri": "https://localhost:8080/", |
|
|
3660 |
"height": 1000 |
|
|
3661 |
} |
|
|
3662 |
}, |
|
|
3663 |
"source": [ |
|
|
3664 |
"# Wavelet generation and feature extraction on these images\n", |
|
|
3665 |
"import radiomics\n", |
|
|
3666 |
"\n", |
|
|
3667 |
"all_wavelet = radiomics.imageoperations.getWaveletImage(scanimg, scanmask)\n", |
|
|
3668 |
"for wavelet in all_wavelet:\n", |
|
|
3669 |
" image = wavelet[0]\n", |
|
|
3670 |
" name = wavelet[1]\n", |
|
|
3671 |
" array = sitk.GetArrayFromImage(image)\n", |
|
|
3672 |
" # print(array)\n", |
|
|
3673 |
" plt.imshow(array[:,:,0])\n", |
|
|
3674 |
" print(name, array.shape)\n", |
|
|
3675 |
" result = extractor.execute(image, scanmask)\n", |
|
|
3676 |
" for key, value in result.items():\n", |
|
|
3677 |
" print(key, value)" |
|
|
3678 |
], |
|
|
3679 |
"execution_count": 0, |
|
|
3680 |
"outputs": [ |
|
|
3681 |
{ |
|
|
3682 |
"output_type": "stream", |
|
|
3683 |
"text": [ |
|
|
3684 |
"wavelet-LLH (92, 92, 92)\n" |
|
|
3685 |
], |
|
|
3686 |
"name": "stdout" |
|
|
3687 |
}, |
|
|
3688 |
{ |
|
|
3689 |
"output_type": "stream", |
|
|
3690 |
"text": [ |
|
|
3691 |
"GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\n" |
|
|
3692 |
], |
|
|
3693 |
"name": "stderr" |
|
|
3694 |
}, |
|
|
3695 |
{ |
|
|
3696 |
"output_type": "stream", |
|
|
3697 |
"text": [ |
|
|
3698 |
"diagnostics_Versions_PyRadiomics v3.0\n", |
|
|
3699 |
"diagnostics_Versions_Numpy 1.18.1\n", |
|
|
3700 |
"diagnostics_Versions_SimpleITK 1.2.4\n", |
|
|
3701 |
"diagnostics_Versions_PyWavelet 1.0.0\n", |
|
|
3702 |
"diagnostics_Versions_Python 3.6.9\n", |
|
|
3703 |
"diagnostics_Configuration_Settings {'minimumROIDimensions': 2, 'minimumROISize': None, 'normalize': False, 'normalizeScale': 1, 'removeOutliers': None, 'resampledPixelSpacing': None, 'interpolator': 'sitkBSpline', 'preCrop': False, 'padDistance': 5, 'distances': [1], 'force2D': False, 'force2Ddimension': 0, 'resegmentRange': None, 'label': 1, 'additionalInfo': True}\n", |
|
|
3704 |
"diagnostics_Configuration_EnabledImageTypes {'Original': {}}\n", |
|
|
3705 |
"diagnostics_Image-original_Hash 88e08a6e78007a9629d0b22b6457a1cafcf03a82\n", |
|
|
3706 |
"diagnostics_Image-original_Dimensionality 3D\n", |
|
|
3707 |
"diagnostics_Image-original_Spacing (1.0, 1.0, 1.0)\n", |
|
|
3708 |
"diagnostics_Image-original_Size (92, 92, 92)\n", |
|
|
3709 |
"diagnostics_Image-original_Mean -3.115245475169457e-14\n", |
|
|
3710 |
"diagnostics_Image-original_Minimum -1129.136648580352\n", |
|
|
3711 |
"diagnostics_Image-original_Maximum 1244.412421105968\n", |
|
|
3712 |
"diagnostics_Mask-original_Hash acd42c92421d3a9df6f4cb58f1548237e3807fb3\n", |
|
|
3713 |
"diagnostics_Mask-original_Spacing (1.0, 1.0, 1.0)\n", |
|
|
3714 |
"diagnostics_Mask-original_Size (92, 92, 92)\n", |
|
|
3715 |
"diagnostics_Mask-original_BoundingBox (3, 1, 7, 85, 89, 78)\n", |
|
|
3716 |
"diagnostics_Mask-original_VoxelNum 184422\n", |
|
|
3717 |
"diagnostics_Mask-original_VolumeNum 1\n", |
|
|
3718 |
"diagnostics_Mask-original_CenterOfMassIndex (42.265206970968755, 46.79903156890176, 48.9044636757003)\n", |
|
|
3719 |
"diagnostics_Mask-original_CenterOfMass (42.265206970968755, 46.79903156890176, 48.9044636757003)\n", |
|
|
3720 |
"original_shape_Elongation 0.8459346778065613\n", |
|
|
3721 |
"original_shape_Flatness 0.6621105014018883\n", |
|
|
3722 |
"original_shape_LeastAxisLength 53.515666406965074\n", |
|
|
3723 |
"original_shape_MajorAxisLength 80.82588373640986\n", |
|
|
3724 |
"original_shape_Maximum2DDiameterColumn 83.81527307120105\n", |
|
|
3725 |
"original_shape_Maximum2DDiameterRow 94.82615672903758\n", |
|
|
3726 |
"original_shape_Maximum2DDiameterSlice 91.67878707749138\n", |
|
|
3727 |
"original_shape_Maximum3DDiameter 100.12991560967181\n", |
|
|
3728 |
"original_shape_MeshVolume 184317.125\n", |
|
|
3729 |
"original_shape_MinorAxisLength 68.37341791699046\n", |
|
|
3730 |
"original_shape_Sphericity 0.5997393163148063\n", |
|
|
3731 |
"original_shape_SurfaceArea 26115.523102962834\n", |
|
|
3732 |
"original_shape_SurfaceVolumeRatio 0.1416879907548625\n", |
|
|
3733 |
"original_shape_VoxelVolume 184422.0\n", |
|
|
3734 |
"original_firstorder_10Percentile -23.85034733601277\n", |
|
|
3735 |
"original_firstorder_90Percentile 20.80220990778991\n", |
|
|
3736 |
"original_firstorder_Energy 136242721.5994512\n", |
|
|
3737 |
"original_firstorder_Entropy 1.8831535342753585\n", |
|
|
3738 |
"original_firstorder_InterquartileRange 21.59062659083733\n", |
|
|
3739 |
"original_firstorder_Kurtosis 19.57297321176867\n", |
|
|
3740 |
"original_firstorder_Maximum 251.51714189521303\n", |
|
|
3741 |
"original_firstorder_MeanAbsoluteDeviation 16.460530532765095\n", |
|
|
3742 |
"original_firstorder_Mean -2.323390673514758\n", |
|
|
3743 |
"original_firstorder_Median -0.5334456598838166\n", |
|
|
3744 |
"original_firstorder_Minimum -333.5432556695371\n", |
|
|
3745 |
"original_firstorder_Range 585.0603975647501\n", |
|
|
3746 |
"original_firstorder_RobustMeanAbsoluteDeviation 9.102204546193335\n", |
|
|
3747 |
"original_firstorder_RootMeanSquared 27.18005251637844\n", |
|
|
3748 |
"original_firstorder_Skewness -1.9761689462996372\n", |
|
|
3749 |
"original_firstorder_TotalEnergy 136242721.5994512\n", |
|
|
3750 |
"original_firstorder_Uniformity 0.3575746942918066\n", |
|
|
3751 |
"original_firstorder_Variance 733.3571105713147\n", |
|
|
3752 |
"original_glcm_Autocorrelation 208.51034852519444\n", |
|
|
3753 |
"original_glcm_ClusterProminence 214.87828384133655\n", |
|
|
3754 |
"original_glcm_ClusterShade -12.169463130457128\n", |
|
|
3755 |
"original_glcm_ClusterTendency 3.224550447933553\n", |
|
|
3756 |
"original_glcm_Contrast 1.1797493435642588\n", |
|
|
3757 |
"original_glcm_Correlation 0.45470892381496925\n", |
|
|
3758 |
"original_glcm_DifferenceAverage 0.6884543978629802\n", |
|
|
3759 |
"original_glcm_DifferenceEntropy 1.460556148098775\n", |
|
|
3760 |
"original_glcm_DifferenceVariance 0.6734441813735241\n", |
|
|
3761 |
"original_glcm_Id 0.7112934023067529\n", |
|
|
3762 |
"original_glcm_Idm 0.697596093081272\n", |
|
|
3763 |
"original_glcm_Idmn 0.9981477525725831\n", |
|
|
3764 |
"original_glcm_Idn 0.9741712994740125\n", |
|
|
3765 |
"original_glcm_Imc1 -0.12026008050553418\n", |
|
|
3766 |
"original_glcm_Imc2 0.5250613942770092\n", |
|
|
3767 |
"original_glcm_InverseVariance 0.4570980423233669\n", |
|
|
3768 |
"original_glcm_JointAverage 14.422174168423592\n", |
|
|
3769 |
"original_glcm_JointEnergy 0.1592549615092642\n", |
|
|
3770 |
"original_glcm_JointEntropy 3.421652231609412\n", |
|
|
3771 |
"original_glcm_MCC 0.6196453167840171\n", |
|
|
3772 |
"original_glcm_MaximumProbability 0.2217180168922737\n", |
|
|
3773 |
"original_glcm_SumAverage 28.84434833684719\n", |
|
|
3774 |
"original_glcm_SumEntropy 2.4642291047573885\n", |
|
|
3775 |
"original_glcm_SumSquares 1.1010749478744528\n", |
|
|
3776 |
"original_gldm_DependenceEntropy 5.8159985122038105\n", |
|
|
3777 |
"original_gldm_DependenceNonUniformity 10504.890718027133\n", |
|
|
3778 |
"original_gldm_DependenceNonUniformityNormalized 0.05696115820253079\n", |
|
|
3779 |
"original_gldm_DependenceVariance 25.325820204106154\n", |
|
|
3780 |
"original_gldm_GrayLevelNonUniformity 65944.64027068354\n", |
|
|
3781 |
"original_gldm_GrayLevelVariance 1.2614158765080594\n", |
|
|
3782 |
"original_gldm_HighGrayLevelEmphasis 208.83013414885426\n", |
|
|
3783 |
"original_gldm_LargeDependenceEmphasis 180.95192547526867\n", |
|
|
3784 |
"original_gldm_LargeDependenceHighGrayLevelEmphasis 38048.94882931538\n", |
|
|
3785 |
"original_gldm_LargeDependenceLowGrayLevelEmphasis 0.8675574945665191\n", |
|
|
3786 |
"original_gldm_LowGrayLevelEmphasis 0.004973204946324459\n", |
|
|
3787 |
"original_gldm_SmallDependenceEmphasis 0.02526889946070429\n", |
|
|
3788 |
"original_gldm_SmallDependenceHighGrayLevelEmphasis 5.091423043820126\n", |
|
|
3789 |
"original_gldm_SmallDependenceLowGrayLevelEmphasis 0.0001629887725060729\n", |
|
|
3790 |
"original_glrlm_GrayLevelNonUniformity 30987.67880033157\n", |
|
|
3791 |
"original_glrlm_GrayLevelNonUniformityNormalized 0.29818790337136414\n", |
|
|
3792 |
"original_glrlm_GrayLevelVariance 1.8787152281781785\n", |
|
|
3793 |
"original_glrlm_HighGrayLevelRunEmphasis 207.764319870827\n", |
|
|
3794 |
"original_glrlm_LongRunEmphasis 6.2577883245398755\n", |
|
|
3795 |
"original_glrlm_LongRunHighGrayLevelEmphasis 1312.7873048113106\n", |
|
|
3796 |
"original_glrlm_LongRunLowGrayLevelEmphasis 0.030387004387029085\n", |
|
|
3797 |
"original_glrlm_LowGrayLevelRunEmphasis 0.0051040327349459284\n", |
|
|
3798 |
"original_glrlm_RunEntropy 3.8841386379138325\n", |
|
|
3799 |
"original_glrlm_RunLengthNonUniformity 43464.05516163069\n", |
|
|
3800 |
"original_glrlm_RunLengthNonUniformityNormalized 0.4039517208606063\n", |
|
|
3801 |
"original_glrlm_RunPercentage 0.5586531057949868\n", |
|
|
3802 |
"original_glrlm_RunVariance 2.3265784263964187\n", |
|
|
3803 |
"original_glrlm_ShortRunEmphasis 0.6425157579738212\n", |
|
|
3804 |
"original_glrlm_ShortRunHighGrayLevelEmphasis 132.76040055525\n", |
|
|
3805 |
"original_glrlm_ShortRunLowGrayLevelEmphasis 0.0033691001193200103\n", |
|
|
3806 |
"original_glszm_GrayLevelNonUniformity 679.2770189312246\n", |
|
|
3807 |
"original_glszm_GrayLevelNonUniformityNormalized 0.16277906037172887\n", |
|
|
3808 |
"original_glszm_GrayLevelVariance 9.453992375873359\n", |
|
|
3809 |
"original_glszm_HighGrayLevelZoneEmphasis 201.78097292115984\n", |
|
|
3810 |
"original_glszm_LargeAreaEmphasis 2842947.7661155043\n", |
|
|
3811 |
"original_glszm_LargeAreaHighGrayLevelEmphasis 597972687.5885454\n", |
|
|
3812 |
"original_glszm_LargeAreaLowGrayLevelEmphasis 13581.456634399401\n", |
|
|
3813 |
"original_glszm_LowGrayLevelZoneEmphasis 0.006939328403185489\n", |
|
|
3814 |
"original_glszm_SizeZoneNonUniformity 673.1361131080757\n", |
|
|
3815 |
"original_glszm_SizeZoneNonUniformityNormalized 0.16130747977667761\n", |
|
|
3816 |
"original_glszm_SmallAreaEmphasis 0.3866074939431698\n", |
|
|
3817 |
"original_glszm_SmallAreaHighGrayLevelEmphasis 77.33745904736678\n", |
|
|
3818 |
"original_glszm_SmallAreaLowGrayLevelEmphasis 0.003033127668840202\n", |
|
|
3819 |
"original_glszm_ZoneEntropy 6.61138822179211\n", |
|
|
3820 |
"original_glszm_ZonePercentage 0.022627452256238408\n", |
|
|
3821 |
"original_glszm_ZoneVariance 2840994.647202248\n", |
|
|
3822 |
"original_ngtdm_Busyness 45.59785349040249\n", |
|
|
3823 |
"original_ngtdm_Coarseness 3.4493420383565154e-05\n", |
|
|
3824 |
"original_ngtdm_Complexity 169.78636481668826\n", |
|
|
3825 |
"original_ngtdm_Contrast 0.0023391379988210365\n", |
|
|
3826 |
"original_ngtdm_Strength 0.026921914513303807\n", |
|
|
3827 |
"wavelet-LHL (92, 92, 92)\n" |
|
|
3828 |
], |
|
|
3829 |
"name": "stdout" |
|
|
3830 |
}, |
|
|
3831 |
{ |
|
|
3832 |
"output_type": "stream", |
|
|
3833 |
"text": [ |
|
|
3834 |
"GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\n" |
|
|
3835 |
], |
|
|
3836 |
"name": "stderr" |
|
|
3837 |
}, |
|
|
3838 |
{ |
|
|
3839 |
"output_type": "stream", |
|
|
3840 |
"text": [ |
|
|
3841 |
"diagnostics_Versions_PyRadiomics v3.0\n", |
|
|
3842 |
"diagnostics_Versions_Numpy 1.18.1\n", |
|
|
3843 |
"diagnostics_Versions_SimpleITK 1.2.4\n", |
|
|
3844 |
"diagnostics_Versions_PyWavelet 1.0.0\n", |
|
|
3845 |
"diagnostics_Versions_Python 3.6.9\n", |
|
|
3846 |
"diagnostics_Configuration_Settings {'minimumROIDimensions': 2, 'minimumROISize': None, 'normalize': False, 'normalizeScale': 1, 'removeOutliers': None, 'resampledPixelSpacing': None, 'interpolator': 'sitkBSpline', 'preCrop': False, 'padDistance': 5, 'distances': [1], 'force2D': False, 'force2Ddimension': 0, 'resegmentRange': None, 'label': 1, 'additionalInfo': True}\n", |
|
|
3847 |
"diagnostics_Configuration_EnabledImageTypes {'Original': {}}\n", |
|
|
3848 |
"diagnostics_Image-original_Hash dcdee9a43cd91a9a6732295dddd7d9d688e3159c\n", |
|
|
3849 |
"diagnostics_Image-original_Dimensionality 3D\n", |
|
|
3850 |
"diagnostics_Image-original_Spacing (1.0, 1.0, 1.0)\n", |
|
|
3851 |
"diagnostics_Image-original_Size (92, 92, 92)\n", |
|
|
3852 |
"diagnostics_Image-original_Mean -3.1528545424686685e-14\n", |
|
|
3853 |
"diagnostics_Image-original_Minimum -1392.512265258323\n", |
|
|
3854 |
"diagnostics_Image-original_Maximum 1521.369718287886\n", |
|
|
3855 |
"diagnostics_Mask-original_Hash acd42c92421d3a9df6f4cb58f1548237e3807fb3\n", |
|
|
3856 |
"diagnostics_Mask-original_Spacing (1.0, 1.0, 1.0)\n", |
|
|
3857 |
"diagnostics_Mask-original_Size (92, 92, 92)\n", |
|
|
3858 |
"diagnostics_Mask-original_BoundingBox (3, 1, 7, 85, 89, 78)\n", |
|
|
3859 |
"diagnostics_Mask-original_VoxelNum 184422\n", |
|
|
3860 |
"diagnostics_Mask-original_VolumeNum 1\n", |
|
|
3861 |
"diagnostics_Mask-original_CenterOfMassIndex (42.265206970968755, 46.79903156890176, 48.9044636757003)\n", |
|
|
3862 |
"diagnostics_Mask-original_CenterOfMass (42.265206970968755, 46.79903156890176, 48.9044636757003)\n", |
|
|
3863 |
"original_shape_Elongation 0.8459346778065613\n", |
|
|
3864 |
"original_shape_Flatness 0.6621105014018883\n", |
|
|
3865 |
"original_shape_LeastAxisLength 53.515666406965074\n", |
|
|
3866 |
"original_shape_MajorAxisLength 80.82588373640986\n", |
|
|
3867 |
"original_shape_Maximum2DDiameterColumn 83.81527307120105\n", |
|
|
3868 |
"original_shape_Maximum2DDiameterRow 94.82615672903758\n", |
|
|
3869 |
"original_shape_Maximum2DDiameterSlice 91.67878707749138\n", |
|
|
3870 |
"original_shape_Maximum3DDiameter 100.12991560967181\n", |
|
|
3871 |
"original_shape_MeshVolume 184317.125\n", |
|
|
3872 |
"original_shape_MinorAxisLength 68.37341791699046\n", |
|
|
3873 |
"original_shape_Sphericity 0.5997393163148063\n", |
|
|
3874 |
"original_shape_SurfaceArea 26115.523102962834\n", |
|
|
3875 |
"original_shape_SurfaceVolumeRatio 0.1416879907548625\n", |
|
|
3876 |
"original_shape_VoxelVolume 184422.0\n", |
|
|
3877 |
"original_firstorder_10Percentile -38.23827958345323\n", |
|
|
3878 |
"original_firstorder_90Percentile 37.28204245858349\n", |
|
|
3879 |
"original_firstorder_Energy 200478817.30042648\n", |
|
|
3880 |
"original_firstorder_Entropy 2.4271877286007704\n", |
|
|
3881 |
"original_firstorder_InterquartileRange 37.11288630044717\n", |
|
|
3882 |
"original_firstorder_Kurtosis 5.856189795930498\n", |
|
|
3883 |
"original_firstorder_Maximum 241.90972646001134\n", |
|
|
3884 |
"original_firstorder_MeanAbsoluteDeviation 24.31419052488978\n", |
|
|
3885 |
"original_firstorder_Mean -0.6754290930460958\n", |
|
|
3886 |
"original_firstorder_Median -0.5137693665954447\n", |
|
|
3887 |
"original_firstorder_Minimum -288.86152390578894\n", |
|
|
3888 |
"original_firstorder_Range 530.7712503658003\n", |
|
|
3889 |
"original_firstorder_RobustMeanAbsoluteDeviation 15.584965590347066\n", |
|
|
3890 |
"original_firstorder_RootMeanSquared 32.97067831103282\n", |
|
|
3891 |
"original_firstorder_Skewness -0.1663213214884285\n", |
|
|
3892 |
"original_firstorder_TotalEnergy 200478817.30042648\n", |
|
|
3893 |
"original_firstorder_Uniformity 0.23463213509331882\n", |
|
|
3894 |
"original_firstorder_Variance 1086.6094238298774\n", |
|
|
3895 |
"original_glcm_Autocorrelation 156.1095202991698\n", |
|
|
3896 |
"original_glcm_ClusterProminence 125.30067765804529\n", |
|
|
3897 |
"original_glcm_ClusterShade -1.6719921425133388\n", |
|
|
3898 |
"original_glcm_ClusterTendency 4.396636377624954\n", |
|
|
3899 |
"original_glcm_Contrast 2.5384570806393465\n", |
|
|
3900 |
"original_glcm_Correlation 0.2653667680860789\n", |
|
|
3901 |
"original_glcm_DifferenceAverage 1.1076885620607535\n", |
|
|
3902 |
"original_glcm_DifferenceEntropy 1.889299035137122\n", |
|
|
3903 |
"original_glcm_DifferenceVariance 1.1885462139213026\n", |
|
|
3904 |
"original_glcm_Id 0.6083279208843286\n", |
|
|
3905 |
"original_glcm_Idm 0.5716224160776942\n", |
|
|
3906 |
"original_glcm_Idmn 0.9948973744541529\n", |
|
|
3907 |
"original_glcm_Idn 0.9542473255485431\n", |
|
|
3908 |
"original_glcm_Imc1 -0.09466862823891037\n", |
|
|
3909 |
"original_glcm_Imc2 0.4263028958534801\n", |
|
|
3910 |
"original_glcm_InverseVariance 0.454372839608978\n", |
|
|
3911 |
"original_glcm_JointAverage 12.475775230005583\n", |
|
|
3912 |
"original_glcm_JointEnergy 0.07055457613753592\n", |
|
|
3913 |
"original_glcm_JointEntropy 4.567435832343083\n", |
|
|
3914 |
"original_glcm_MCC 0.42185274157760805\n", |
|
|
3915 |
"original_glcm_MaximumProbability 0.12914565108731713\n", |
|
|
3916 |
"original_glcm_SumAverage 24.951550460011163\n", |
|
|
3917 |
"original_glcm_SumEntropy 3.016796883502309\n", |
|
|
3918 |
"original_glcm_SumSquares 1.7337733645660747\n", |
|
|
3919 |
"original_gldm_DependenceEntropy 6.2455268920622204\n", |
|
|
3920 |
"original_gldm_DependenceNonUniformity 11633.780546789429\n", |
|
|
3921 |
"original_gldm_DependenceNonUniformityNormalized 0.06308239009873784\n", |
|
|
3922 |
"original_gldm_DependenceVariance 20.17642945675274\n", |
|
|
3923 |
"original_gldm_GrayLevelNonUniformity 43271.32761818005\n", |
|
|
3924 |
"original_gldm_GrayLevelVariance 1.822888789938282\n", |
|
|
3925 |
"original_gldm_HighGrayLevelEmphasis 157.38311589723568\n", |
|
|
3926 |
"original_gldm_LargeDependenceEmphasis 103.89264838251401\n", |
|
|
3927 |
"original_gldm_LargeDependenceHighGrayLevelEmphasis 16266.629637461909\n", |
|
|
3928 |
"original_gldm_LargeDependenceLowGrayLevelEmphasis 0.6763450388707649\n", |
|
|
3929 |
"original_gldm_LowGrayLevelEmphasis 0.006717799578643404\n", |
|
|
3930 |
"original_gldm_SmallDependenceEmphasis 0.038956841154031134\n", |
|
|
3931 |
"original_gldm_SmallDependenceHighGrayLevelEmphasis 6.204933872203366\n", |
|
|
3932 |
"original_gldm_SmallDependenceLowGrayLevelEmphasis 0.00031388889828199524\n", |
|
|
3933 |
"original_glrlm_GrayLevelNonUniformity 26249.655579367067\n", |
|
|
3934 |
"original_glrlm_GrayLevelNonUniformityNormalized 0.20700885708189765\n", |
|
|
3935 |
"original_glrlm_GrayLevelVariance 2.2167065218804836\n", |
|
|
3936 |
"original_glrlm_HighGrayLevelRunEmphasis 157.65311414370854\n", |
|
|
3937 |
"original_glrlm_LongRunEmphasis 3.5116469785314264\n", |
|
|
3938 |
"original_glrlm_LongRunHighGrayLevelEmphasis 551.3780410829886\n", |
|
|
3939 |
"original_glrlm_LongRunLowGrayLevelEmphasis 0.02322105540797002\n", |
|
|
3940 |
"original_glrlm_LowGrayLevelRunEmphasis 0.006798293107700222\n", |
|
|
3941 |
"original_glrlm_RunEntropy 3.83782946782725\n", |
|
|
3942 |
"original_glrlm_RunLengthNonUniformity 71970.21951757456\n", |
|
|
3943 |
"original_glrlm_RunLengthNonUniformityNormalized 0.5471601703444068\n", |
|
|
3944 |
"original_glrlm_RunPercentage 0.6865516628668531\n", |
|
|
3945 |
"original_glrlm_RunVariance 1.0828031423463158\n", |
|
|
3946 |
"original_glrlm_ShortRunEmphasis 0.7569971266575225\n", |
|
|
3947 |
"original_glrlm_ShortRunHighGrayLevelEmphasis 119.50250862342037\n", |
|
|
3948 |
"original_glrlm_ShortRunLowGrayLevelEmphasis 0.0051972692956276005\n", |
|
|
3949 |
"original_glszm_GrayLevelNonUniformity 649.3541438481197\n", |
|
|
3950 |
"original_glszm_GrayLevelNonUniformityNormalized 0.11853854396643296\n", |
|
|
3951 |
"original_glszm_GrayLevelVariance 9.016389265115826\n", |
|
|
3952 |
"original_glszm_HighGrayLevelZoneEmphasis 162.4587440671778\n", |
|
|
3953 |
"original_glszm_LargeAreaEmphasis 1289718.1398320554\n", |
|
|
3954 |
"original_glszm_LargeAreaHighGrayLevelEmphasis 200509821.05677253\n", |
|
|
3955 |
"original_glszm_LargeAreaLowGrayLevelEmphasis 8379.329982645046\n", |
|
|
3956 |
"original_glszm_LowGrayLevelZoneEmphasis 0.008884726205121981\n", |
|
|
3957 |
"original_glszm_SizeZoneNonUniformity 835.8853596202994\n", |
|
|
3958 |
"original_glszm_SizeZoneNonUniformityNormalized 0.15258951435200793\n", |
|
|
3959 |
"original_glszm_SmallAreaEmphasis 0.3724737064071854\n", |
|
|
3960 |
"original_glszm_SmallAreaHighGrayLevelEmphasis 59.51740408527065\n", |
|
|
3961 |
"original_glszm_SmallAreaLowGrayLevelEmphasis 0.003971239667081267\n", |
|
|
3962 |
"original_glszm_ZoneEntropy 6.863012827418467\n", |
|
|
3963 |
"original_glszm_ZonePercentage 0.029703614536226698\n", |
|
|
3964 |
"original_glszm_ZoneVariance 1288584.7445534403\n", |
|
|
3965 |
"original_ngtdm_Busyness 56.07256994586304\n", |
|
|
3966 |
"original_ngtdm_Coarseness 3.984008558475323e-05\n", |
|
|
3967 |
"original_ngtdm_Complexity 191.12909256324738\n", |
|
|
3968 |
"original_ngtdm_Contrast 0.006500479266524036\n", |
|
|
3969 |
"original_ngtdm_Strength 0.012459399428519856\n", |
|
|
3970 |
"wavelet-LHH (92, 92, 92)\n" |
|
|
3971 |
], |
|
|
3972 |
"name": "stdout" |
|
|
3973 |
}, |
|
|
3974 |
{ |
|
|
3975 |
"output_type": "stream", |
|
|
3976 |
"text": [ |
|
|
3977 |
"GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\n" |
|
|
3978 |
], |
|
|
3979 |
"name": "stderr" |
|
|
3980 |
}, |
|
|
3981 |
{ |
|
|
3982 |
"output_type": "stream", |
|
|
3983 |
"text": [ |
|
|
3984 |
"diagnostics_Versions_PyRadiomics v3.0\n", |
|
|
3985 |
"diagnostics_Versions_Numpy 1.18.1\n", |
|
|
3986 |
"diagnostics_Versions_SimpleITK 1.2.4\n", |
|
|
3987 |
"diagnostics_Versions_PyWavelet 1.0.0\n", |
|
|
3988 |
"diagnostics_Versions_Python 3.6.9\n", |
|
|
3989 |
"diagnostics_Configuration_Settings {'minimumROIDimensions': 2, 'minimumROISize': None, 'normalize': False, 'normalizeScale': 1, 'removeOutliers': None, 'resampledPixelSpacing': None, 'interpolator': 'sitkBSpline', 'preCrop': False, 'padDistance': 5, 'distances': [1], 'force2D': False, 'force2Ddimension': 0, 'resegmentRange': None, 'label': 1, 'additionalInfo': True}\n", |
|
|
3990 |
"diagnostics_Configuration_EnabledImageTypes {'Original': {}}\n", |
|
|
3991 |
"diagnostics_Image-original_Hash cf40d7e0225408947e2058fa5f95bbba92108676\n", |
|
|
3992 |
"diagnostics_Image-original_Dimensionality 3D\n", |
|
|
3993 |
"diagnostics_Image-original_Spacing (1.0, 1.0, 1.0)\n", |
|
|
3994 |
"diagnostics_Image-original_Size (92, 92, 92)\n", |
|
|
3995 |
"diagnostics_Image-original_Mean -2.6279627771188057e-18\n", |
|
|
3996 |
"diagnostics_Image-original_Minimum -404.20765775966606\n", |
|
|
3997 |
"diagnostics_Image-original_Maximum 454.673050984171\n", |
|
|
3998 |
"diagnostics_Mask-original_Hash acd42c92421d3a9df6f4cb58f1548237e3807fb3\n", |
|
|
3999 |
"diagnostics_Mask-original_Spacing (1.0, 1.0, 1.0)\n", |
|
|
4000 |
"diagnostics_Mask-original_Size (92, 92, 92)\n", |
|
|
4001 |
"diagnostics_Mask-original_BoundingBox (3, 1, 7, 85, 89, 78)\n", |
|
|
4002 |
"diagnostics_Mask-original_VoxelNum 184422\n", |
|
|
4003 |
"diagnostics_Mask-original_VolumeNum 1\n", |
|
|
4004 |
"diagnostics_Mask-original_CenterOfMassIndex (42.265206970968755, 46.79903156890176, 48.9044636757003)\n", |
|
|
4005 |
"diagnostics_Mask-original_CenterOfMass (42.265206970968755, 46.79903156890176, 48.9044636757003)\n", |
|
|
4006 |
"original_shape_Elongation 0.8459346778065613\n", |
|
|
4007 |
"original_shape_Flatness 0.6621105014018883\n", |
|
|
4008 |
"original_shape_LeastAxisLength 53.515666406965074\n", |
|
|
4009 |
"original_shape_MajorAxisLength 80.82588373640986\n", |
|
|
4010 |
"original_shape_Maximum2DDiameterColumn 83.81527307120105\n", |
|
|
4011 |
"original_shape_Maximum2DDiameterRow 94.82615672903758\n", |
|
|
4012 |
"original_shape_Maximum2DDiameterSlice 91.67878707749138\n", |
|
|
4013 |
"original_shape_Maximum3DDiameter 100.12991560967181\n", |
|
|
4014 |
"original_shape_MeshVolume 184317.125\n", |
|
|
4015 |
"original_shape_MinorAxisLength 68.37341791699046\n", |
|
|
4016 |
"original_shape_Sphericity 0.5997393163148063\n", |
|
|
4017 |
"original_shape_SurfaceArea 26115.523102962834\n", |
|
|
4018 |
"original_shape_SurfaceVolumeRatio 0.1416879907548625\n", |
|
|
4019 |
"original_shape_VoxelVolume 184422.0\n", |
|
|
4020 |
"original_firstorder_10Percentile -11.133494249880535\n", |
|
|
4021 |
"original_firstorder_90Percentile 11.28347615460951\n", |
|
|
4022 |
"original_firstorder_Energy 16722745.27198185\n", |
|
|
4023 |
"original_firstorder_Entropy 1.1261194405910802\n", |
|
|
4024 |
"original_firstorder_InterquartileRange 11.366759060208935\n", |
|
|
4025 |
"original_firstorder_Kurtosis 6.822881235611824\n", |
|
|
4026 |
"original_firstorder_Maximum 125.51175517534683\n", |
|
|
4027 |
"original_firstorder_MeanAbsoluteDeviation 7.160289874268283\n", |
|
|
4028 |
"original_firstorder_Mean 0.04748822350341286\n", |
|
|
4029 |
"original_firstorder_Median -0.009457003504083489\n", |
|
|
4030 |
"original_firstorder_Minimum -93.92895874039192\n", |
|
|
4031 |
"original_firstorder_Range 219.44071391573874\n", |
|
|
4032 |
"original_firstorder_RobustMeanAbsoluteDeviation 4.7439802038212635\n", |
|
|
4033 |
"original_firstorder_RootMeanSquared 9.522421989450622\n", |
|
|
4034 |
"original_firstorder_Skewness 0.17065533946185216\n", |
|
|
4035 |
"original_firstorder_TotalEnergy 16722745.27198185\n", |
|
|
4036 |
"original_firstorder_Uniformity 0.4839634270289807\n", |
|
|
4037 |
"original_firstorder_Variance 90.67426541380125\n", |
|
|
4038 |
"original_glcm_Autocorrelation 20.259341588801153\n", |
|
|
4039 |
"original_glcm_ClusterProminence 1.221520530343987\n", |
|
|
4040 |
"original_glcm_ClusterShade 0.026349243029748854\n", |
|
|
4041 |
"original_glcm_ClusterTendency 0.6053822359522825\n", |
|
|
4042 |
"original_glcm_Contrast 0.5254243936193431\n", |
|
|
4043 |
"original_glcm_Correlation 0.06986151880602641\n", |
|
|
4044 |
"original_glcm_DifferenceAverage 0.49607149116424193\n", |
|
|
4045 |
"original_glcm_DifferenceEntropy 1.048861316655826\n", |
|
|
4046 |
"original_glcm_DifferenceVariance 0.26734709879611107\n", |
|
|
4047 |
"original_glcm_Id 0.7564079608436097\n", |
|
|
4048 |
"original_glcm_Idm 0.7548351566544458\n", |
|
|
4049 |
"original_glcm_Idmn 0.9948224056540252\n", |
|
|
4050 |
"original_glcm_Idn 0.9551176739598147\n", |
|
|
4051 |
"original_glcm_Imc1 -0.04401623169724621\n", |
|
|
4052 |
"original_glcm_Imc2 0.19670250782057883\n", |
|
|
4053 |
"original_glcm_InverseVariance 0.4741758561010926\n", |
|
|
4054 |
"original_glcm_JointAverage 4.498816722127272\n", |
|
|
4055 |
"original_glcm_JointEnergy 0.24844457432321498\n", |
|
|
4056 |
"original_glcm_JointEntropy 2.1774273882539648\n", |
|
|
4057 |
"original_glcm_MCC 0.3898687089176107\n", |
|
|
4058 |
"original_glcm_MaximumProbability 0.2745812417478539\n", |
|
|
4059 |
"original_glcm_SumAverage 8.997633444254546\n", |
|
|
4060 |
"original_glcm_SumEntropy 1.613000911907538\n", |
|
|
4061 |
"original_glcm_SumSquares 0.2827016573929063\n", |
|
|
4062 |
"original_gldm_DependenceEntropy 4.745282975100425\n", |
|
|
4063 |
"original_gldm_DependenceNonUniformity 17393.248495298827\n", |
|
|
4064 |
"original_gldm_DependenceNonUniformityNormalized 0.09431222140145333\n", |
|
|
4065 |
"original_gldm_DependenceVariance 10.894277198126746\n", |
|
|
4066 |
"original_gldm_GrayLevelNonUniformity 89253.50313953866\n", |
|
|
4067 |
"original_gldm_GrayLevelVariance 0.2878797319933297\n", |
|
|
4068 |
"original_gldm_HighGrayLevelEmphasis 20.54583509559597\n", |
|
|
4069 |
"original_gldm_LargeDependenceEmphasis 201.80724642396243\n", |
|
|
4070 |
"original_gldm_LargeDependenceHighGrayLevelEmphasis 4128.472904534166\n", |
|
|
4071 |
"original_gldm_LargeDependenceLowGrayLevelEmphasis 10.373225946057156\n", |
|
|
4072 |
"original_gldm_LowGrayLevelEmphasis 0.051598817392325684\n", |
|
|
4073 |
"original_gldm_SmallDependenceEmphasis 0.009856622452392952\n", |
|
|
4074 |
"original_gldm_SmallDependenceHighGrayLevelEmphasis 0.2151943094551541\n", |
|
|
4075 |
"original_gldm_SmallDependenceLowGrayLevelEmphasis 0.0005669596216792106\n", |
|
|
4076 |
"original_glrlm_GrayLevelNonUniformity 44135.239396268626\n", |
|
|
4077 |
"original_glrlm_GrayLevelNonUniformityNormalized 0.47122531953586166\n", |
|
|
4078 |
"original_glrlm_GrayLevelVariance 0.319143532467766\n", |
|
|
4079 |
"original_glrlm_HighGrayLevelRunEmphasis 20.615938702564538\n", |
|
|
4080 |
"original_glrlm_LongRunEmphasis 9.104206012274703\n", |
|
|
4081 |
"original_glrlm_LongRunHighGrayLevelEmphasis 186.62510355834254\n", |
|
|
4082 |
"original_glrlm_LongRunLowGrayLevelEmphasis 0.4681965310907935\n", |
|
|
4083 |
"original_glrlm_LowGrayLevelRunEmphasis 0.05180161237642226\n", |
|
|
4084 |
"original_glrlm_RunEntropy 3.132788932052445\n", |
|
|
4085 |
"original_glrlm_RunLengthNonUniformity 35152.4533881111\n", |
|
|
4086 |
"original_glrlm_RunLengthNonUniformityNormalized 0.3590301200164072\n", |
|
|
4087 |
"original_glrlm_RunPercentage 0.507033617714556\n", |
|
|
4088 |
"original_glrlm_RunVariance 3.2349578952723186\n", |
|
|
4089 |
"original_glrlm_ShortRunEmphasis 0.5649726262779\n", |
|
|
4090 |
"original_glrlm_ShortRunHighGrayLevelEmphasis 11.69418044127549\n", |
|
|
4091 |
"original_glrlm_ShortRunLowGrayLevelEmphasis 0.029430049943561767\n", |
|
|
4092 |
"original_glszm_GrayLevelNonUniformity 365.9626274065685\n", |
|
|
4093 |
"original_glszm_GrayLevelNonUniformityNormalized 0.414453711672218\n", |
|
|
4094 |
"original_glszm_GrayLevelVariance 2.4667553344987554\n", |
|
|
4095 |
"original_glszm_HighGrayLevelZoneEmphasis 23.80634201585504\n", |
|
|
4096 |
"original_glszm_LargeAreaEmphasis 18622897.139297847\n", |
|
|
4097 |
"original_glszm_LargeAreaHighGrayLevelEmphasis 381457122.4190261\n", |
|
|
4098 |
"original_glszm_LargeAreaLowGrayLevelEmphasis 955205.6273349313\n", |
|
|
4099 |
"original_glszm_LowGrayLevelZoneEmphasis 0.07011843949441694\n", |
|
|
4100 |
"original_glszm_SizeZoneNonUniformity 201.38958097395243\n", |
|
|
4101 |
"original_glszm_SizeZoneNonUniformityNormalized 0.22807427063867772\n", |
|
|
4102 |
"original_glszm_SmallAreaEmphasis 0.4346219433345503\n", |
|
|
4103 |
"original_glszm_SmallAreaHighGrayLevelEmphasis 10.634385840156046\n", |
|
|
4104 |
"original_glszm_SmallAreaLowGrayLevelEmphasis 0.029643327578996084\n", |
|
|
4105 |
"original_glszm_ZoneEntropy 4.1707864720000645\n", |
|
|
4106 |
"original_glszm_ZonePercentage 0.00478793202546334\n", |
|
|
4107 |
"original_glszm_ZoneVariance 18579275.292530738\n", |
|
|
4108 |
"original_ngtdm_Busyness 578.4766256003373\n", |
|
|
4109 |
"original_ngtdm_Coarseness 2.388140262228302e-05\n", |
|
|
4110 |
"original_ngtdm_Complexity 24.457443805570627\n", |
|
|
4111 |
"original_ngtdm_Contrast 0.0030906083928206336\n", |
|
|
4112 |
"original_ngtdm_Strength 0.0021406380723725618\n", |
|
|
4113 |
"wavelet-HLL (92, 92, 92)\n" |
|
|
4114 |
], |
|
|
4115 |
"name": "stdout" |
|
|
4116 |
}, |
|
|
4117 |
{ |
|
|
4118 |
"output_type": "stream", |
|
|
4119 |
"text": [ |
|
|
4120 |
"GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\n" |
|
|
4121 |
], |
|
|
4122 |
"name": "stderr" |
|
|
4123 |
}, |
|
|
4124 |
{ |
|
|
4125 |
"output_type": "stream", |
|
|
4126 |
"text": [ |
|
|
4127 |
"diagnostics_Versions_PyRadiomics v3.0\n", |
|
|
4128 |
"diagnostics_Versions_Numpy 1.18.1\n", |
|
|
4129 |
"diagnostics_Versions_SimpleITK 1.2.4\n", |
|
|
4130 |
"diagnostics_Versions_PyWavelet 1.0.0\n", |
|
|
4131 |
"diagnostics_Versions_Python 3.6.9\n", |
|
|
4132 |
"diagnostics_Configuration_Settings {'minimumROIDimensions': 2, 'minimumROISize': None, 'normalize': False, 'normalizeScale': 1, 'removeOutliers': None, 'resampledPixelSpacing': None, 'interpolator': 'sitkBSpline', 'preCrop': False, 'padDistance': 5, 'distances': [1], 'force2D': False, 'force2Ddimension': 0, 'resegmentRange': None, 'label': 1, 'additionalInfo': True}\n", |
|
|
4133 |
"diagnostics_Configuration_EnabledImageTypes {'Original': {}}\n", |
|
|
4134 |
"diagnostics_Image-original_Hash e505c13842c9d2f23ed3a9fddb7363eb7b8b8e07\n", |
|
|
4135 |
"diagnostics_Image-original_Dimensionality 3D\n", |
|
|
4136 |
"diagnostics_Image-original_Spacing (1.0, 1.0, 1.0)\n", |
|
|
4137 |
"diagnostics_Image-original_Size (92, 92, 92)\n", |
|
|
4138 |
"diagnostics_Image-original_Mean -3.2181338678419596e-14\n", |
|
|
4139 |
"diagnostics_Image-original_Minimum -1558.2805060228166\n", |
|
|
4140 |
"diagnostics_Image-original_Maximum 1466.5664341598476\n", |
|
|
4141 |
"diagnostics_Mask-original_Hash acd42c92421d3a9df6f4cb58f1548237e3807fb3\n", |
|
|
4142 |
"diagnostics_Mask-original_Spacing (1.0, 1.0, 1.0)\n", |
|
|
4143 |
"diagnostics_Mask-original_Size (92, 92, 92)\n", |
|
|
4144 |
"diagnostics_Mask-original_BoundingBox (3, 1, 7, 85, 89, 78)\n", |
|
|
4145 |
"diagnostics_Mask-original_VoxelNum 184422\n", |
|
|
4146 |
"diagnostics_Mask-original_VolumeNum 1\n", |
|
|
4147 |
"diagnostics_Mask-original_CenterOfMassIndex (42.265206970968755, 46.79903156890176, 48.9044636757003)\n", |
|
|
4148 |
"diagnostics_Mask-original_CenterOfMass (42.265206970968755, 46.79903156890176, 48.9044636757003)\n", |
|
|
4149 |
"original_shape_Elongation 0.8459346778065613\n", |
|
|
4150 |
"original_shape_Flatness 0.6621105014018883\n", |
|
|
4151 |
"original_shape_LeastAxisLength 53.515666406965074\n", |
|
|
4152 |
"original_shape_MajorAxisLength 80.82588373640986\n", |
|
|
4153 |
"original_shape_Maximum2DDiameterColumn 83.81527307120105\n", |
|
|
4154 |
"original_shape_Maximum2DDiameterRow 94.82615672903758\n", |
|
|
4155 |
"original_shape_Maximum2DDiameterSlice 91.67878707749138\n", |
|
|
4156 |
"original_shape_Maximum3DDiameter 100.12991560967181\n", |
|
|
4157 |
"original_shape_MeshVolume 184317.125\n", |
|
|
4158 |
"original_shape_MinorAxisLength 68.37341791699046\n", |
|
|
4159 |
"original_shape_Sphericity 0.5997393163148063\n", |
|
|
4160 |
"original_shape_SurfaceArea 26115.523102962834\n", |
|
|
4161 |
"original_shape_SurfaceVolumeRatio 0.1416879907548625\n", |
|
|
4162 |
"original_shape_VoxelVolume 184422.0\n", |
|
|
4163 |
"original_firstorder_10Percentile -11.33832854714845\n", |
|
|
4164 |
"original_firstorder_90Percentile 8.737373736679647\n", |
|
|
4165 |
"original_firstorder_Energy 65763015.427597806\n", |
|
|
4166 |
"original_firstorder_Entropy 1.475308705283878\n", |
|
|
4167 |
"original_firstorder_InterquartileRange 5.5430281011729345\n", |
|
|
4168 |
"original_firstorder_Kurtosis 26.922897957165624\n", |
|
|
4169 |
"original_firstorder_Maximum 233.05728415990907\n", |
|
|
4170 |
"original_firstorder_MeanAbsoluteDeviation 8.574999957842847\n", |
|
|
4171 |
"original_firstorder_Mean -1.8549932998100054\n", |
|
|
4172 |
"original_firstorder_Median -0.11593369120846014\n", |
|
|
4173 |
"original_firstorder_Minimum -211.74303505365555\n", |
|
|
4174 |
"original_firstorder_Range 444.80031921356465\n", |
|
|
4175 |
"original_firstorder_RobustMeanAbsoluteDeviation 2.821925408684005\n", |
|
|
4176 |
"original_firstorder_RootMeanSquared 18.8835870915994\n", |
|
|
4177 |
"original_firstorder_Skewness -2.9178611267642616\n", |
|
|
4178 |
"original_firstorder_TotalEnergy 65763015.427597806\n", |
|
|
4179 |
"original_firstorder_Uniformity 0.4361395293648338\n", |
|
|
4180 |
"original_firstorder_Variance 353.1488613036794\n", |
|
|
4181 |
"original_glcm_Autocorrelation 89.36108422120085\n", |
|
|
4182 |
"original_glcm_ClusterProminence 55.39008441970993\n", |
|
|
4183 |
"original_glcm_ClusterShade -4.583129174927439\n", |
|
|
4184 |
"original_glcm_ClusterTendency 1.9337541268723026\n", |
|
|
4185 |
"original_glcm_Contrast 0.7395232574683376\n", |
|
|
4186 |
"original_glcm_Correlation 0.43494686275335037\n", |
|
|
4187 |
"original_glcm_DifferenceAverage 0.4853624351194768\n", |
|
|
4188 |
"original_glcm_DifferenceEntropy 1.2194290075220933\n", |
|
|
4189 |
"original_glcm_DifferenceVariance 0.49099273482148387\n", |
|
|
4190 |
"original_glcm_Id 0.7855113517456823\n", |
|
|
4191 |
"original_glcm_Idm 0.779777378982111\n", |
|
|
4192 |
"original_glcm_Idmn 0.9979944401231998\n", |
|
|
4193 |
"original_glcm_Idn 0.9762896711472288\n", |
|
|
4194 |
"original_glcm_Imc1 -0.14033868454993262\n", |
|
|
4195 |
"original_glcm_Imc2 0.5285589222004885\n", |
|
|
4196 |
"original_glcm_InverseVariance 0.3762271403100869\n", |
|
|
4197 |
"original_glcm_JointAverage 9.437290891789168\n", |
|
|
4198 |
"original_glcm_JointEnergy 0.22535059255426143\n", |
|
|
4199 |
"original_glcm_JointEntropy 2.6564774522049985\n", |
|
|
4200 |
"original_glcm_MCC 0.523080622618541\n", |
|
|
4201 |
"original_glcm_MaximumProbability 0.2915504466128727\n", |
|
|
4202 |
"original_glcm_SumAverage 18.87458178357834\n", |
|
|
4203 |
"original_glcm_SumEntropy 2.140411209041056\n", |
|
|
4204 |
"original_glcm_SumSquares 0.6683193460851599\n", |
|
|
4205 |
"original_gldm_DependenceEntropy 5.652527142215339\n", |
|
|
4206 |
"original_gldm_DependenceNonUniformity 10211.629827244038\n", |
|
|
4207 |
"original_gldm_DependenceNonUniformityNormalized 0.055370996015898524\n", |
|
|
4208 |
"original_gldm_DependenceVariance 29.565961825602123\n", |
|
|
4209 |
"original_gldm_GrayLevelNonUniformity 80433.72428452136\n", |
|
|
4210 |
"original_gldm_GrayLevelVariance 0.7449479537824315\n", |
|
|
4211 |
"original_gldm_HighGrayLevelEmphasis 89.63165999718038\n", |
|
|
4212 |
"original_gldm_LargeDependenceEmphasis 273.78051425534915\n", |
|
|
4213 |
"original_gldm_LargeDependenceHighGrayLevelEmphasis 24795.15578401709\n", |
|
|
4214 |
"original_gldm_LargeDependenceLowGrayLevelEmphasis 3.0660104156114705\n", |
|
|
4215 |
"original_gldm_LowGrayLevelEmphasis 0.011783655620602377\n", |
|
|
4216 |
"original_gldm_SmallDependenceEmphasis 0.015393491849129259\n", |
|
|
4217 |
"original_gldm_SmallDependenceHighGrayLevelEmphasis 1.2734329853466142\n", |
|
|
4218 |
"original_gldm_SmallDependenceLowGrayLevelEmphasis 0.00028063174712759876\n", |
|
|
4219 |
"original_glrlm_GrayLevelNonUniformity 30864.08809548723\n", |
|
|
4220 |
"original_glrlm_GrayLevelNonUniformityNormalized 0.381551210562017\n", |
|
|
4221 |
"original_glrlm_GrayLevelVariance 1.2485005907256774\n", |
|
|
4222 |
"original_glrlm_HighGrayLevelRunEmphasis 88.6154992736869\n", |
|
|
4223 |
"original_glrlm_LongRunEmphasis 10.141271901022147\n", |
|
|
4224 |
"original_glrlm_LongRunHighGrayLevelEmphasis 914.65490585239\n", |
|
|
4225 |
"original_glrlm_LongRunLowGrayLevelEmphasis 0.11540919505581537\n", |
|
|
4226 |
"original_glrlm_LowGrayLevelRunEmphasis 0.012449539561702761\n", |
|
|
4227 |
"original_glrlm_RunEntropy 3.9547936407615225\n", |
|
|
4228 |
"original_glrlm_RunLengthNonUniformity 25090.6345489079\n", |
|
|
4229 |
"original_glrlm_RunLengthNonUniformityNormalized 0.29728894394718447\n", |
|
|
4230 |
"original_glrlm_RunPercentage 0.43740901928103015\n", |
|
|
4231 |
"original_glrlm_RunVariance 4.052754998347607\n", |
|
|
4232 |
"original_glrlm_ShortRunEmphasis 0.5284594031642066\n", |
|
|
4233 |
"original_glrlm_ShortRunHighGrayLevelEmphasis 46.05340996589684\n", |
|
|
4234 |
"original_glrlm_ShortRunLowGrayLevelEmphasis 0.007079687220600762\n", |
|
|
4235 |
"original_glszm_GrayLevelNonUniformity 298.8534258456201\n", |
|
|
4236 |
"original_glszm_GrayLevelNonUniformityNormalized 0.12959818987234176\n", |
|
|
4237 |
"original_glszm_GrayLevelVariance 8.02439749542842\n", |
|
|
4238 |
"original_glszm_HighGrayLevelZoneEmphasis 79.04119687771032\n", |
|
|
4239 |
"original_glszm_LargeAreaEmphasis 6348744.554206418\n", |
|
|
4240 |
"original_glszm_LargeAreaHighGrayLevelEmphasis 572031449.6426713\n", |
|
|
4241 |
"original_glszm_LargeAreaLowGrayLevelEmphasis 71246.47689228796\n", |
|
|
4242 |
"original_glszm_LowGrayLevelZoneEmphasis 0.0222239731542388\n", |
|
|
4243 |
"original_glszm_SizeZoneNonUniformity 494.9002601908066\n", |
|
|
4244 |
"original_glszm_SizeZoneNonUniformityNormalized 0.214614163135649\n", |
|
|
4245 |
"original_glszm_SmallAreaEmphasis 0.46737762692489493\n", |
|
|
4246 |
"original_glszm_SmallAreaHighGrayLevelEmphasis 40.09181899997995\n", |
|
|
4247 |
"original_glszm_SmallAreaLowGrayLevelEmphasis 0.009810032446517663\n", |
|
|
4248 |
"original_glszm_ZoneEntropy 6.279459590502976\n", |
|
|
4249 |
"original_glszm_ZonePercentage 0.01250393120126666\n", |
|
|
4250 |
"original_glszm_ZoneVariance 6342348.577858281\n", |
|
|
4251 |
"original_ngtdm_Busyness 96.08146742647237\n", |
|
|
4252 |
"original_ngtdm_Coarseness 3.319409430273022e-05\n", |
|
|
4253 |
"original_ngtdm_Complexity 76.34992808501931\n", |
|
|
4254 |
"original_ngtdm_Contrast 0.0018847967848832387\n", |
|
|
4255 |
"original_ngtdm_Strength 0.01479815632391325\n", |
|
|
4256 |
"wavelet-HLH (92, 92, 92)\n" |
|
|
4257 |
], |
|
|
4258 |
"name": "stdout" |
|
|
4259 |
}, |
|
|
4260 |
{ |
|
|
4261 |
"output_type": "stream", |
|
|
4262 |
"text": [ |
|
|
4263 |
"GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\n" |
|
|
4264 |
], |
|
|
4265 |
"name": "stderr" |
|
|
4266 |
}, |
|
|
4267 |
{ |
|
|
4268 |
"output_type": "stream", |
|
|
4269 |
"text": [ |
|
|
4270 |
"diagnostics_Versions_PyRadiomics v3.0\n", |
|
|
4271 |
"diagnostics_Versions_Numpy 1.18.1\n", |
|
|
4272 |
"diagnostics_Versions_SimpleITK 1.2.4\n", |
|
|
4273 |
"diagnostics_Versions_PyWavelet 1.0.0\n", |
|
|
4274 |
"diagnostics_Versions_Python 3.6.9\n", |
|
|
4275 |
"diagnostics_Configuration_Settings {'minimumROIDimensions': 2, 'minimumROISize': None, 'normalize': False, 'normalizeScale': 1, 'removeOutliers': None, 'resampledPixelSpacing': None, 'interpolator': 'sitkBSpline', 'preCrop': False, 'padDistance': 5, 'distances': [1], 'force2D': False, 'force2Ddimension': 0, 'resegmentRange': None, 'label': 1, 'additionalInfo': True}\n", |
|
|
4276 |
"diagnostics_Configuration_EnabledImageTypes {'Original': {}}\n", |
|
|
4277 |
"diagnostics_Image-original_Hash 85ee1bde3f6617ee0bae1adcd482b4b8d26be8b4\n", |
|
|
4278 |
"diagnostics_Image-original_Dimensionality 3D\n", |
|
|
4279 |
"diagnostics_Image-original_Spacing (1.0, 1.0, 1.0)\n", |
|
|
4280 |
"diagnostics_Image-original_Size (92, 92, 92)\n", |
|
|
4281 |
"diagnostics_Image-original_Mean -3.0294570902897345e-18\n", |
|
|
4282 |
"diagnostics_Image-original_Minimum -579.4979390804076\n", |
|
|
4283 |
"diagnostics_Image-original_Maximum 561.8934122398533\n", |
|
|
4284 |
"diagnostics_Mask-original_Hash acd42c92421d3a9df6f4cb58f1548237e3807fb3\n", |
|
|
4285 |
"diagnostics_Mask-original_Spacing (1.0, 1.0, 1.0)\n", |
|
|
4286 |
"diagnostics_Mask-original_Size (92, 92, 92)\n", |
|
|
4287 |
"diagnostics_Mask-original_BoundingBox (3, 1, 7, 85, 89, 78)\n", |
|
|
4288 |
"diagnostics_Mask-original_VoxelNum 184422\n", |
|
|
4289 |
"diagnostics_Mask-original_VolumeNum 1\n", |
|
|
4290 |
"diagnostics_Mask-original_CenterOfMassIndex (42.265206970968755, 46.79903156890176, 48.9044636757003)\n", |
|
|
4291 |
"diagnostics_Mask-original_CenterOfMass (42.265206970968755, 46.79903156890176, 48.9044636757003)\n", |
|
|
4292 |
"original_shape_Elongation 0.8459346778065613\n", |
|
|
4293 |
"original_shape_Flatness 0.6621105014018883\n", |
|
|
4294 |
"original_shape_LeastAxisLength 53.515666406965074\n", |
|
|
4295 |
"original_shape_MajorAxisLength 80.82588373640986\n", |
|
|
4296 |
"original_shape_Maximum2DDiameterColumn 83.81527307120105\n", |
|
|
4297 |
"original_shape_Maximum2DDiameterRow 94.82615672903758\n", |
|
|
4298 |
"original_shape_Maximum2DDiameterSlice 91.67878707749138\n", |
|
|
4299 |
"original_shape_Maximum3DDiameter 100.12991560967181\n", |
|
|
4300 |
"original_shape_MeshVolume 184317.125\n", |
|
|
4301 |
"original_shape_MinorAxisLength 68.37341791699046\n", |
|
|
4302 |
"original_shape_Sphericity 0.5997393163148063\n", |
|
|
4303 |
"original_shape_SurfaceArea 26115.523102962834\n", |
|
|
4304 |
"original_shape_SurfaceVolumeRatio 0.1416879907548625\n", |
|
|
4305 |
"original_shape_VoxelVolume 184422.0\n", |
|
|
4306 |
"original_firstorder_10Percentile -2.4528001360924243\n", |
|
|
4307 |
"original_firstorder_90Percentile 2.4966100836354794\n", |
|
|
4308 |
"original_firstorder_Energy 1852833.5107308289\n", |
|
|
4309 |
"original_firstorder_Entropy 1.0169324456122997\n", |
|
|
4310 |
"original_firstorder_InterquartileRange 1.6335750186947382\n", |
|
|
4311 |
"original_firstorder_Kurtosis 31.942059705051456\n", |
|
|
4312 |
"original_firstorder_Maximum 51.37142274676806\n", |
|
|
4313 |
"original_firstorder_MeanAbsoluteDeviation 1.702457444479685\n", |
|
|
4314 |
"original_firstorder_Mean 0.05597151941796191\n", |
|
|
4315 |
"original_firstorder_Median -0.0022874557253750724\n", |
|
|
4316 |
"original_firstorder_Minimum -47.341161753648684\n", |
|
|
4317 |
"original_firstorder_Range 98.71258450041674\n", |
|
|
4318 |
"original_firstorder_RobustMeanAbsoluteDeviation 0.7688690873283788\n", |
|
|
4319 |
"original_firstorder_RootMeanSquared 3.169653836053313\n", |
|
|
4320 |
"original_firstorder_Skewness 1.5473950278310074\n", |
|
|
4321 |
"original_firstorder_TotalEnergy 1852833.5107308289\n", |
|
|
4322 |
"original_firstorder_Uniformity 0.4983907403758854\n", |
|
|
4323 |
"original_firstorder_Variance 10.04357262942153\n", |
|
|
4324 |
"original_glcm_Autocorrelation 6.266516173047642\n", |
|
|
4325 |
"original_glcm_ClusterProminence 0.5855526584843711\n", |
|
|
4326 |
"original_glcm_ClusterShade 0.010768976378755167\n", |
|
|
4327 |
"original_glcm_ClusterTendency 0.5576064542655566\n", |
|
|
4328 |
"original_glcm_Contrast 0.4508687076121536\n", |
|
|
4329 |
"original_glcm_Correlation 0.10576243926501311\n", |
|
|
4330 |
"original_glcm_DifferenceAverage 0.45018692168414876\n", |
|
|
4331 |
"original_glcm_DifferenceEntropy 0.9813337980087145\n", |
|
|
4332 |
"original_glcm_DifferenceVariance 0.2430628305657521\n", |
|
|
4333 |
"original_glcm_Id 0.7750201701459265\n", |
|
|
4334 |
"original_glcm_Idm 0.7749747177507262\n", |
|
|
4335 |
"original_glcm_Idmn 0.9826643212132832\n", |
|
|
4336 |
"original_glcm_Idn 0.9249850793842611\n", |
|
|
4337 |
"original_glcm_Imc1 -0.02423988157384203\n", |
|
|
4338 |
"original_glcm_Imc2 0.1450616313548404\n", |
|
|
4339 |
"original_glcm_InverseVariance 0.44959035899714467\n", |
|
|
4340 |
"original_glcm_JointAverage 2.497965389676092\n", |
|
|
4341 |
"original_glcm_JointEnergy 0.2567232859425508\n", |
|
|
4342 |
"original_glcm_JointEntropy 1.9986284042093891\n", |
|
|
4343 |
"original_glcm_MCC 0.16472687243021047\n", |
|
|
4344 |
"original_glcm_MaximumProbability 0.27983615680682844\n", |
|
|
4345 |
"original_glcm_SumAverage 4.995930779352184\n", |
|
|
4346 |
"original_glcm_SumEntropy 1.5450248718755182\n", |
|
|
4347 |
"original_glcm_SumSquares 0.2521187904694276\n", |
|
|
4348 |
"original_gldm_DependenceEntropy 4.818188792817496\n", |
|
|
4349 |
"original_gldm_DependenceNonUniformity 16011.664443504571\n", |
|
|
4350 |
"original_gldm_DependenceNonUniformityNormalized 0.08682079385054153\n", |
|
|
4351 |
"original_gldm_DependenceVariance 11.865489008071185\n", |
|
|
4352 |
"original_gldm_GrayLevelNonUniformity 91914.21712160154\n", |
|
|
4353 |
"original_gldm_GrayLevelVariance 0.2532750708694629\n", |
|
|
4354 |
"original_gldm_HighGrayLevelEmphasis 6.504088449317327\n", |
|
|
4355 |
"original_gldm_LargeDependenceEmphasis 226.2801834922081\n", |
|
|
4356 |
"original_gldm_LargeDependenceHighGrayLevelEmphasis 1460.0270737764474\n", |
|
|
4357 |
"original_gldm_LargeDependenceLowGrayLevelEmphasis 41.159038385996375\n", |
|
|
4358 |
"original_gldm_LowGrayLevelEmphasis 0.1808439008843245\n", |
|
|
4359 |
"original_gldm_SmallDependenceEmphasis 0.006377855447301732\n", |
|
|
4360 |
"original_gldm_SmallDependenceHighGrayLevelEmphasis 0.04362971576383602\n", |
|
|
4361 |
"original_gldm_SmallDependenceLowGrayLevelEmphasis 0.001221225840332901\n", |
|
|
4362 |
"original_glrlm_GrayLevelNonUniformity 43551.6005081189\n", |
|
|
4363 |
"original_glrlm_GrayLevelNonUniformityNormalized 0.4968521846180083\n", |
|
|
4364 |
"original_glrlm_GrayLevelVariance 0.25642786412067775\n", |
|
|
4365 |
"original_glrlm_HighGrayLevelRunEmphasis 6.539113604393748\n", |
|
|
4366 |
"original_glrlm_LongRunEmphasis 7.246330499615427\n", |
|
|
4367 |
"original_glrlm_LongRunHighGrayLevelEmphasis 46.86782556269574\n", |
|
|
4368 |
"original_glrlm_LongRunLowGrayLevelEmphasis 1.3159480777567132\n", |
|
|
4369 |
"original_glrlm_LowGrayLevelRunEmphasis 0.180274763045163\n", |
|
|
4370 |
"original_glrlm_RunEntropy 3.112778445975675\n", |
|
|
4371 |
"original_glrlm_RunLengthNonUniformity 28266.872188865505\n", |
|
|
4372 |
"original_glrlm_RunLengthNonUniformityNormalized 0.31377821727868455\n", |
|
|
4373 |
"original_glrlm_RunPercentage 0.4752728483086032\n", |
|
|
4374 |
"original_glrlm_RunVariance 2.342383969656721\n", |
|
|
4375 |
"original_glrlm_ShortRunEmphasis 0.5348064282381623\n", |
|
|
4376 |
"original_glrlm_ShortRunHighGrayLevelEmphasis 3.5183480251187897\n", |
|
|
4377 |
"original_glrlm_ShortRunLowGrayLevelEmphasis 0.09616258585272226\n", |
|
|
4378 |
"original_glszm_GrayLevelNonUniformity 48.36842105263158\n", |
|
|
4379 |
"original_glszm_GrayLevelNonUniformityNormalized 0.4242843951985226\n", |
|
|
4380 |
"original_glszm_GrayLevelVariance 1.8664973838104033\n", |
|
|
4381 |
"original_glszm_HighGrayLevelZoneEmphasis 10.605263157894736\n", |
|
|
4382 |
"original_glszm_LargeAreaEmphasis 148642345.71929824\n", |
|
|
4383 |
"original_glszm_LargeAreaHighGrayLevelEmphasis 964290044.5877193\n", |
|
|
4384 |
"original_glszm_LargeAreaLowGrayLevelEmphasis 26890571.154576022\n", |
|
|
4385 |
"original_glszm_LowGrayLevelZoneEmphasis 0.3446345029239766\n", |
|
|
4386 |
"original_glszm_SizeZoneNonUniformity 26.80701754385965\n", |
|
|
4387 |
"original_glszm_SizeZoneNonUniformityNormalized 0.23514927670052324\n", |
|
|
4388 |
"original_glszm_SmallAreaEmphasis 0.45820500269433134\n", |
|
|
4389 |
"original_glszm_SmallAreaHighGrayLevelEmphasis 4.3437231879889415\n", |
|
|
4390 |
"original_glszm_SmallAreaLowGrayLevelEmphasis 0.19524376620989997\n", |
|
|
4391 |
"original_glszm_ZoneEntropy 3.891042169072997\n", |
|
|
4392 |
"original_glszm_ZonePercentage 0.0006181475095162183\n", |
|
|
4393 |
"original_glszm_ZoneVariance 146025273.2289935\n", |
|
|
4394 |
"original_ngtdm_Busyness 2570.5411722573817\n", |
|
|
4395 |
"original_ngtdm_Coarseness 2.438833241035695e-05\n", |
|
|
4396 |
"original_ngtdm_Complexity 5.352194375344888\n", |
|
|
4397 |
"original_ngtdm_Contrast 0.011322861020072492\n", |
|
|
4398 |
"original_ngtdm_Strength 0.000303601489323549\n", |
|
|
4399 |
"wavelet-HHL (92, 92, 92)\n" |
|
|
4400 |
], |
|
|
4401 |
"name": "stdout" |
|
|
4402 |
}, |
|
|
4403 |
{ |
|
|
4404 |
"output_type": "stream", |
|
|
4405 |
"text": [ |
|
|
4406 |
"GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\n" |
|
|
4407 |
], |
|
|
4408 |
"name": "stderr" |
|
|
4409 |
}, |
|
|
4410 |
{ |
|
|
4411 |
"output_type": "stream", |
|
|
4412 |
"text": [ |
|
|
4413 |
"diagnostics_Versions_PyRadiomics v3.0\n", |
|
|
4414 |
"diagnostics_Versions_Numpy 1.18.1\n", |
|
|
4415 |
"diagnostics_Versions_SimpleITK 1.2.4\n", |
|
|
4416 |
"diagnostics_Versions_PyWavelet 1.0.0\n", |
|
|
4417 |
"diagnostics_Versions_Python 3.6.9\n", |
|
|
4418 |
"diagnostics_Configuration_Settings {'minimumROIDimensions': 2, 'minimumROISize': None, 'normalize': False, 'normalizeScale': 1, 'removeOutliers': None, 'resampledPixelSpacing': None, 'interpolator': 'sitkBSpline', 'preCrop': False, 'padDistance': 5, 'distances': [1], 'force2D': False, 'force2Ddimension': 0, 'resegmentRange': None, 'label': 1, 'additionalInfo': True}\n", |
|
|
4419 |
"diagnostics_Configuration_EnabledImageTypes {'Original': {}}\n", |
|
|
4420 |
"diagnostics_Image-original_Hash a9f0c0ebc6baf3e01ee7a6ad0118e35421455762\n", |
|
|
4421 |
"diagnostics_Image-original_Dimensionality 3D\n", |
|
|
4422 |
"diagnostics_Image-original_Spacing (1.0, 1.0, 1.0)\n", |
|
|
4423 |
"diagnostics_Image-original_Size (92, 92, 92)\n", |
|
|
4424 |
"diagnostics_Image-original_Mean -1.3732930484596537e-17\n", |
|
|
4425 |
"diagnostics_Image-original_Minimum -235.00433127415184\n", |
|
|
4426 |
"diagnostics_Image-original_Maximum 250.15534378713684\n", |
|
|
4427 |
"diagnostics_Mask-original_Hash acd42c92421d3a9df6f4cb58f1548237e3807fb3\n", |
|
|
4428 |
"diagnostics_Mask-original_Spacing (1.0, 1.0, 1.0)\n", |
|
|
4429 |
"diagnostics_Mask-original_Size (92, 92, 92)\n", |
|
|
4430 |
"diagnostics_Mask-original_BoundingBox (3, 1, 7, 85, 89, 78)\n", |
|
|
4431 |
"diagnostics_Mask-original_VoxelNum 184422\n", |
|
|
4432 |
"diagnostics_Mask-original_VolumeNum 1\n", |
|
|
4433 |
"diagnostics_Mask-original_CenterOfMassIndex (42.265206970968755, 46.79903156890176, 48.9044636757003)\n", |
|
|
4434 |
"diagnostics_Mask-original_CenterOfMass (42.265206970968755, 46.79903156890176, 48.9044636757003)\n", |
|
|
4435 |
"original_shape_Elongation 0.8459346778065613\n", |
|
|
4436 |
"original_shape_Flatness 0.6621105014018883\n", |
|
|
4437 |
"original_shape_LeastAxisLength 53.515666406965074\n", |
|
|
4438 |
"original_shape_MajorAxisLength 80.82588373640986\n", |
|
|
4439 |
"original_shape_Maximum2DDiameterColumn 83.81527307120105\n", |
|
|
4440 |
"original_shape_Maximum2DDiameterRow 94.82615672903758\n", |
|
|
4441 |
"original_shape_Maximum2DDiameterSlice 91.67878707749138\n", |
|
|
4442 |
"original_shape_Maximum3DDiameter 100.12991560967181\n", |
|
|
4443 |
"original_shape_MeshVolume 184317.125\n", |
|
|
4444 |
"original_shape_MinorAxisLength 68.37341791699046\n", |
|
|
4445 |
"original_shape_Sphericity 0.5997393163148063\n", |
|
|
4446 |
"original_shape_SurfaceArea 26115.523102962834\n", |
|
|
4447 |
"original_shape_SurfaceVolumeRatio 0.1416879907548625\n", |
|
|
4448 |
"original_shape_VoxelVolume 184422.0\n", |
|
|
4449 |
"original_firstorder_10Percentile -4.3572803124920645\n", |
|
|
4450 |
"original_firstorder_90Percentile 4.3570058118500965\n", |
|
|
4451 |
"original_firstorder_Energy 3518065.4082933646\n", |
|
|
4452 |
"original_firstorder_Entropy 1.0150181474920812\n", |
|
|
4453 |
"original_firstorder_InterquartileRange 2.6278361003124866\n", |
|
|
4454 |
"original_firstorder_Kurtosis 10.551689255141655\n", |
|
|
4455 |
"original_firstorder_Maximum 48.32843442574533\n", |
|
|
4456 |
"original_firstorder_MeanAbsoluteDeviation 2.6745400827739068\n", |
|
|
4457 |
"original_firstorder_Mean 0.010380434543498499\n", |
|
|
4458 |
"original_firstorder_Median -0.0031662658214016114\n", |
|
|
4459 |
"original_firstorder_Minimum -54.16446848518554\n", |
|
|
4460 |
"original_firstorder_Range 102.49290291093087\n", |
|
|
4461 |
"original_firstorder_RobustMeanAbsoluteDeviation 1.2799621434192674\n", |
|
|
4462 |
"original_firstorder_RootMeanSquared 4.367627492843567\n", |
|
|
4463 |
"original_firstorder_Skewness 0.13904829895390447\n", |
|
|
4464 |
"original_firstorder_TotalEnergy 3518065.4082933646\n", |
|
|
4465 |
"original_firstorder_Uniformity 0.498631525705559\n", |
|
|
4466 |
"original_firstorder_Variance 19.076062162821675\n", |
|
|
4467 |
"original_glcm_Autocorrelation 12.26846967576633\n", |
|
|
4468 |
"original_glcm_ClusterProminence 0.5787049669457318\n", |
|
|
4469 |
"original_glcm_ClusterShade 0.0041199568738719255\n", |
|
|
4470 |
"original_glcm_ClusterTendency 0.5566995785091471\n", |
|
|
4471 |
"original_glcm_Contrast 0.4512612097244653\n", |
|
|
4472 |
"original_glcm_Correlation 0.10455474297566678\n", |
|
|
4473 |
"original_glcm_DifferenceAverage 0.45032425474911475\n", |
|
|
4474 |
"original_glcm_DifferenceEntropy 0.9680128846532612\n", |
|
|
4475 |
"original_glcm_DifferenceVariance 0.23859283497284536\n", |
|
|
4476 |
"original_glcm_Id 0.7749934924485937\n", |
|
|
4477 |
"original_glcm_Idm 0.7749315681229778\n", |
|
|
4478 |
"original_glcm_Idmn 0.9826513282096911\n", |
|
|
4479 |
"original_glcm_Idn 0.924968227469365\n", |
|
|
4480 |
"original_glcm_Imc1 -0.03811190488842235\n", |
|
|
4481 |
"original_glcm_Imc2 0.15907565619540184\n", |
|
|
4482 |
"original_glcm_InverseVariance 0.449509512906752\n", |
|
|
4483 |
"original_glcm_JointAverage 3.498872673832608\n", |
|
|
4484 |
"original_glcm_JointEnergy 0.2614569336181821\n", |
|
|
4485 |
"original_glcm_JointEntropy 1.9839749044196284\n", |
|
|
4486 |
"original_glcm_MCC 0.14931512295078145\n", |
|
|
4487 |
"original_glcm_MaximumProbability 0.28275489630928463\n", |
|
|
4488 |
"original_glcm_SumAverage 6.997745347665216\n", |
|
|
4489 |
"original_glcm_SumEntropy 1.5290355231956143\n", |
|
|
4490 |
"original_glcm_SumSquares 0.25199019705840314\n", |
|
|
4491 |
"original_gldm_DependenceEntropy 4.831491146663192\n", |
|
|
4492 |
"original_gldm_DependenceNonUniformity 15948.582923946167\n", |
|
|
4493 |
"original_gldm_DependenceNonUniformityNormalized 0.08647874398903692\n", |
|
|
4494 |
"original_gldm_DependenceVariance 12.243870417951157\n", |
|
|
4495 |
"original_gldm_GrayLevelNonUniformity 91958.6232336706\n", |
|
|
4496 |
"original_gldm_GrayLevelVariance 0.25276522692805736\n", |
|
|
4497 |
"original_gldm_HighGrayLevelEmphasis 12.499880708375356\n", |
|
|
4498 |
"original_gldm_LargeDependenceEmphasis 226.68302046393597\n", |
|
|
4499 |
"original_gldm_LargeDependenceHighGrayLevelEmphasis 2826.6038379369056\n", |
|
|
4500 |
"original_gldm_LargeDependenceLowGrayLevelEmphasis 19.72604690902146\n", |
|
|
4501 |
"original_gldm_LowGrayLevelEmphasis 0.08689965736794478\n", |
|
|
4502 |
"original_gldm_SmallDependenceEmphasis 0.006573816069694546\n", |
|
|
4503 |
"original_gldm_SmallDependenceHighGrayLevelEmphasis 0.08443376687012913\n", |
|
|
4504 |
"original_gldm_SmallDependenceLowGrayLevelEmphasis 0.0005948863051811838\n", |
|
|
4505 |
"original_glrlm_GrayLevelNonUniformity 43582.7965787402\n", |
|
|
4506 |
"original_glrlm_GrayLevelNonUniformityNormalized 0.4971597497837625\n", |
|
|
4507 |
"original_glrlm_GrayLevelVariance 0.25574995941037654\n", |
|
|
4508 |
"original_glrlm_HighGrayLevelRunEmphasis 12.52166435487776\n", |
|
|
4509 |
"original_glrlm_LongRunEmphasis 8.126395758332116\n", |
|
|
4510 |
"original_glrlm_LongRunHighGrayLevelEmphasis 101.40767624045212\n", |
|
|
4511 |
"original_glrlm_LongRunLowGrayLevelEmphasis 0.7068578780736972\n", |
|
|
4512 |
"original_glrlm_LowGrayLevelRunEmphasis 0.08685032895924696\n", |
|
|
4513 |
"original_glrlm_RunEntropy 3.1056247940233295\n", |
|
|
4514 |
"original_glrlm_RunLengthNonUniformity 29006.470342228593\n", |
|
|
4515 |
"original_glrlm_RunLengthNonUniformityNormalized 0.3159353631117385\n", |
|
|
4516 |
"original_glrlm_RunPercentage 0.4752407313327376\n", |
|
|
4517 |
"original_glrlm_RunVariance 2.5947218534803813\n", |
|
|
4518 |
"original_glrlm_ShortRunEmphasis 0.5230642651026437\n", |
|
|
4519 |
"original_glrlm_ShortRunHighGrayLevelEmphasis 6.563861466378782\n", |
|
|
4520 |
"original_glrlm_ShortRunLowGrayLevelEmphasis 0.045413722626630335\n", |
|
|
4521 |
"original_glszm_GrayLevelNonUniformity 59.855172413793106\n", |
|
|
4522 |
"original_glszm_GrayLevelNonUniformityNormalized 0.41279429250891797\n", |
|
|
4523 |
"original_glszm_GrayLevelVariance 2.0204518430439955\n", |
|
|
4524 |
"original_glszm_HighGrayLevelZoneEmphasis 15.889655172413793\n", |
|
|
4525 |
"original_glszm_LargeAreaEmphasis 116928011.42068966\n", |
|
|
4526 |
"original_glszm_LargeAreaHighGrayLevelEmphasis 1460133369.0551724\n", |
|
|
4527 |
"original_glszm_LargeAreaLowGrayLevelEmphasis 10160187.245036399\n", |
|
|
4528 |
"original_glszm_LowGrayLevelZoneEmphasis 0.12849616858237548\n", |
|
|
4529 |
"original_glszm_SizeZoneNonUniformity 50.62758620689655\n", |
|
|
4530 |
"original_glszm_SizeZoneNonUniformityNormalized 0.34915576694411415\n", |
|
|
4531 |
"original_glszm_SmallAreaEmphasis 0.5981585249058416\n", |
|
|
4532 |
"original_glszm_SmallAreaHighGrayLevelEmphasis 9.48004070883262\n", |
|
|
4533 |
"original_glszm_SmallAreaLowGrayLevelEmphasis 0.07927754430090737\n", |
|
|
4534 |
"original_glszm_ZoneEntropy 3.3008845022263307\n", |
|
|
4535 |
"original_glszm_ZonePercentage 0.000786240253332032\n", |
|
|
4536 |
"original_glszm_ZoneVariance 115310343.212176\n", |
|
|
4537 |
"original_ngtdm_Busyness 1871.4748884296816\n", |
|
|
4538 |
"original_ngtdm_Coarseness 2.434513865379978e-05\n", |
|
|
4539 |
"original_ngtdm_Complexity 5.362799156636136\n", |
|
|
4540 |
"original_ngtdm_Contrast 0.011314782129497342\n", |
|
|
4541 |
"original_ngtdm_Strength 0.0003031147221519334\n", |
|
|
4542 |
"wavelet-HHH (92, 92, 92)\n" |
|
|
4543 |
], |
|
|
4544 |
"name": "stdout" |
|
|
4545 |
}, |
|
|
4546 |
{ |
|
|
4547 |
"output_type": "stream", |
|
|
4548 |
"text": [ |
|
|
4549 |
"GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\n" |
|
|
4550 |
], |
|
|
4551 |
"name": "stderr" |
|
|
4552 |
}, |
|
|
4553 |
{ |
|
|
4554 |
"output_type": "stream", |
|
|
4555 |
"text": [ |
|
|
4556 |
"diagnostics_Versions_PyRadiomics v3.0\n", |
|
|
4557 |
"diagnostics_Versions_Numpy 1.18.1\n", |
|
|
4558 |
"diagnostics_Versions_SimpleITK 1.2.4\n", |
|
|
4559 |
"diagnostics_Versions_PyWavelet 1.0.0\n", |
|
|
4560 |
"diagnostics_Versions_Python 3.6.9\n", |
|
|
4561 |
"diagnostics_Configuration_Settings {'minimumROIDimensions': 2, 'minimumROISize': None, 'normalize': False, 'normalizeScale': 1, 'removeOutliers': None, 'resampledPixelSpacing': None, 'interpolator': 'sitkBSpline', 'preCrop': False, 'padDistance': 5, 'distances': [1], 'force2D': False, 'force2Ddimension': 0, 'resegmentRange': None, 'label': 1, 'additionalInfo': True}\n", |
|
|
4562 |
"diagnostics_Configuration_EnabledImageTypes {'Original': {}}\n", |
|
|
4563 |
"diagnostics_Image-original_Hash 91abfae3f172d56f3aba3bd61e3ba8ca3ae6958a\n", |
|
|
4564 |
"diagnostics_Image-original_Dimensionality 3D\n", |
|
|
4565 |
"diagnostics_Image-original_Spacing (1.0, 1.0, 1.0)\n", |
|
|
4566 |
"diagnostics_Image-original_Size (92, 92, 92)\n", |
|
|
4567 |
"diagnostics_Image-original_Mean -4.1061918392481336e-19\n", |
|
|
4568 |
"diagnostics_Image-original_Minimum -132.1837226558246\n", |
|
|
4569 |
"diagnostics_Image-original_Maximum 137.0224927844642\n", |
|
|
4570 |
"diagnostics_Mask-original_Hash acd42c92421d3a9df6f4cb58f1548237e3807fb3\n", |
|
|
4571 |
"diagnostics_Mask-original_Spacing (1.0, 1.0, 1.0)\n", |
|
|
4572 |
"diagnostics_Mask-original_Size (92, 92, 92)\n", |
|
|
4573 |
"diagnostics_Mask-original_BoundingBox (3, 1, 7, 85, 89, 78)\n", |
|
|
4574 |
"diagnostics_Mask-original_VoxelNum 184422\n", |
|
|
4575 |
"diagnostics_Mask-original_VolumeNum 1\n", |
|
|
4576 |
"diagnostics_Mask-original_CenterOfMassIndex (42.265206970968755, 46.79903156890176, 48.9044636757003)\n", |
|
|
4577 |
"diagnostics_Mask-original_CenterOfMass (42.265206970968755, 46.79903156890176, 48.9044636757003)\n", |
|
|
4578 |
"original_shape_Elongation 0.8459346778065613\n", |
|
|
4579 |
"original_shape_Flatness 0.6621105014018883\n", |
|
|
4580 |
"original_shape_LeastAxisLength 53.515666406965074\n", |
|
|
4581 |
"original_shape_MajorAxisLength 80.82588373640986\n", |
|
|
4582 |
"original_shape_Maximum2DDiameterColumn 83.81527307120105\n", |
|
|
4583 |
"original_shape_Maximum2DDiameterRow 94.82615672903758\n", |
|
|
4584 |
"original_shape_Maximum2DDiameterSlice 91.67878707749138\n", |
|
|
4585 |
"original_shape_Maximum3DDiameter 100.12991560967181\n", |
|
|
4586 |
"original_shape_MeshVolume 184317.125\n", |
|
|
4587 |
"original_shape_MinorAxisLength 68.37341791699046\n", |
|
|
4588 |
"original_shape_Sphericity 0.5997393163148063\n", |
|
|
4589 |
"original_shape_SurfaceArea 26115.523102962834\n", |
|
|
4590 |
"original_shape_SurfaceVolumeRatio 0.1416879907548625\n", |
|
|
4591 |
"original_shape_VoxelVolume 184422.0\n", |
|
|
4592 |
"original_firstorder_10Percentile -1.3103119793382996\n", |
|
|
4593 |
"original_firstorder_90Percentile 1.3164819690218246\n", |
|
|
4594 |
"original_firstorder_Energy 309094.56533599563\n", |
|
|
4595 |
"original_firstorder_Entropy 0.9999988584493837\n", |
|
|
4596 |
"original_firstorder_InterquartileRange 0.9717439755957356\n", |
|
|
4597 |
"original_firstorder_Kurtosis 9.834507684330427\n", |
|
|
4598 |
"original_firstorder_Maximum 18.369088916293766\n", |
|
|
4599 |
"original_firstorder_MeanAbsoluteDeviation 0.8372962009331049\n", |
|
|
4600 |
"original_firstorder_Mean -0.0004083089304949379\n", |
|
|
4601 |
"original_firstorder_Median -0.0010351288328073417\n", |
|
|
4602 |
"original_firstorder_Minimum -17.418459503012702\n", |
|
|
4603 |
"original_firstorder_Range 35.78754841930647\n", |
|
|
4604 |
"original_firstorder_RobustMeanAbsoluteDeviation 0.43895995834598184\n", |
|
|
4605 |
"original_firstorder_RootMeanSquared 1.29461108353403\n", |
|
|
4606 |
"original_firstorder_Skewness 0.004904734953746606\n", |
|
|
4607 |
"original_firstorder_TotalEnergy 309094.56533599563\n", |
|
|
4608 |
"original_firstorder_Uniformity 0.5000007912623821\n", |
|
|
4609 |
"original_firstorder_Variance 1.6760176908929727\n", |
|
|
4610 |
"original_glcm_Autocorrelation 2.2531411965100876\n", |
|
|
4611 |
"original_glcm_ClusterProminence 0.5073939922146304\n", |
|
|
4612 |
"original_glcm_ClusterShade 0.0001933887593552982\n", |
|
|
4613 |
"original_glcm_ClusterTendency 0.5073939880545832\n", |
|
|
4614 |
"original_glcm_Contrast 0.4926057651424896\n", |
|
|
4615 |
"original_glcm_Correlation 0.014788224298774253\n", |
|
|
4616 |
"original_glcm_DifferenceAverage 0.4926057651424896\n", |
|
|
4617 |
"original_glcm_DifferenceEntropy 0.9946541749357505\n", |
|
|
4618 |
"original_glcm_DifferenceVariance 0.24815723272548873\n", |
|
|
4619 |
"original_glcm_Id 0.7536971174287552\n", |
|
|
4620 |
"original_glcm_Idm 0.7536971174287552\n", |
|
|
4621 |
"original_glcm_Idmn 0.901478846971502\n", |
|
|
4622 |
"original_glcm_Idn 0.8357980782858369\n", |
|
|
4623 |
"original_glcm_Imc1 -0.005345826131883635\n", |
|
|
4624 |
"original_glcm_Imc2 0.0698651237240808\n", |
|
|
4625 |
"original_glcm_InverseVariance 0.4926057651424896\n", |
|
|
4626 |
"original_glcm_JointAverage 1.4998146930271108\n", |
|
|
4627 |
"original_glcm_JointEnergy 0.2518428906759749\n", |
|
|
4628 |
"original_glcm_JointEntropy 1.9946538186823366\n", |
|
|
4629 |
"original_glcm_MCC 0.05850461237925709\n", |
|
|
4630 |
"original_glcm_MaximumProbability 0.2647178730885922\n", |
|
|
4631 |
"original_glcm_SumAverage 2.9996293860542216\n", |
|
|
4632 |
"original_glcm_SumEntropy 1.5020480535398473\n", |
|
|
4633 |
"original_glcm_SumSquares 0.24999993829926823\n", |
|
|
4634 |
"original_gldm_DependenceEntropy 4.340029888496145\n", |
|
|
4635 |
"original_gldm_DependenceNonUniformity 22368.978917916516\n", |
|
|
4636 |
"original_gldm_DependenceNonUniformityNormalized 0.12129235621518321\n", |
|
|
4637 |
"original_gldm_DependenceVariance 6.3751957435447695\n", |
|
|
4638 |
"original_gldm_GrayLevelNonUniformity 92211.14592619102\n", |
|
|
4639 |
"original_gldm_GrayLevelVariance 0.249999604368809\n", |
|
|
4640 |
"original_gldm_HighGrayLevelEmphasis 2.498113023392003\n", |
|
|
4641 |
"original_gldm_LargeDependenceEmphasis 190.60390842741106\n", |
|
|
4642 |
"original_gldm_LargeDependenceHighGrayLevelEmphasis 476.3535749530967\n", |
|
|
4643 |
"original_gldm_LargeDependenceLowGrayLevelEmphasis 119.16649179598963\n", |
|
|
4644 |
"original_gldm_LowGrayLevelEmphasis 0.6254717441519992\n", |
|
|
4645 |
"original_gldm_SmallDependenceEmphasis 0.006408163678797142\n", |
|
|
4646 |
"original_gldm_SmallDependenceHighGrayLevelEmphasis 0.01600401497944125\n", |
|
|
4647 |
"original_gldm_SmallDependenceLowGrayLevelEmphasis 0.004009200853636112\n", |
|
|
4648 |
"original_glrlm_GrayLevelNonUniformity 47619.74050667098\n", |
|
|
4649 |
"original_glrlm_GrayLevelNonUniformityNormalized 0.5000017281234157\n", |
|
|
4650 |
"original_glrlm_GrayLevelVariance 0.2499991359382922\n", |
|
|
4651 |
"original_glrlm_HighGrayLevelRunEmphasis 2.497361108813341\n", |
|
|
4652 |
"original_glrlm_LongRunEmphasis 5.625370982385132\n", |
|
|
4653 |
"original_glrlm_LongRunHighGrayLevelEmphasis 14.057121555804356\n", |
|
|
4654 |
"original_glrlm_LongRunLowGrayLevelEmphasis 3.5174333390303265\n", |
|
|
4655 |
"original_glrlm_LowGrayLevelRunEmphasis 0.6256597227966647\n", |
|
|
4656 |
"original_glrlm_RunEntropy 2.9091351159807153\n", |
|
|
4657 |
"original_glrlm_RunLengthNonUniformity 33726.49579730632\n", |
|
|
4658 |
"original_glrlm_RunLengthNonUniformityNormalized 0.3516490060182147\n", |
|
|
4659 |
"original_glrlm_RunPercentage 0.516419699635368\n", |
|
|
4660 |
"original_glrlm_RunVariance 1.792914598152639\n", |
|
|
4661 |
"original_glrlm_ShortRunEmphasis 0.5889545835765392\n", |
|
|
4662 |
"original_glrlm_ShortRunHighGrayLevelEmphasis 1.4699875722925972\n", |
|
|
4663 |
"original_glrlm_ShortRunLowGrayLevelEmphasis 0.36869633639752464\n", |
|
|
4664 |
"original_glszm_GrayLevelNonUniformity 3.3333333333333335\n", |
|
|
4665 |
"original_glszm_GrayLevelNonUniformityNormalized 0.5555555555555556\n", |
|
|
4666 |
"original_glszm_GrayLevelVariance 0.2222222222222222\n", |
|
|
4667 |
"original_glszm_HighGrayLevelZoneEmphasis 3.0\n", |
|
|
4668 |
"original_glszm_LargeAreaEmphasis 2834171124.0\n", |
|
|
4669 |
"original_glszm_LargeAreaHighGrayLevelEmphasis 7074639357.5\n", |
|
|
4670 |
"original_glszm_LargeAreaLowGrayLevelEmphasis 1774054065.625\n", |
|
|
4671 |
"original_glszm_LowGrayLevelZoneEmphasis 0.5\n", |
|
|
4672 |
"original_glszm_SizeZoneNonUniformity 3.0\n", |
|
|
4673 |
"original_glszm_SizeZoneNonUniformityNormalized 0.5\n", |
|
|
4674 |
"original_glszm_SmallAreaEmphasis 0.666666666705871\n", |
|
|
4675 |
"original_glszm_SmallAreaHighGrayLevelEmphasis 2.1666666667648267\n", |
|
|
4676 |
"original_glszm_SmallAreaLowGrayLevelEmphasis 0.2916666666911321\n", |
|
|
4677 |
"original_glszm_ZoneEntropy 1.7924812503605767\n", |
|
|
4678 |
"original_glszm_ZonePercentage 3.253407944822201e-05\n", |
|
|
4679 |
"original_glszm_ZoneVariance 1889407955.0\n", |
|
|
4680 |
"original_ngtdm_Busyness 45551.89265651088\n", |
|
|
4681 |
"original_ngtdm_Coarseness 2.2036148035890424e-05\n", |
|
|
4682 |
"original_ngtdm_Complexity 0.49213197931619046\n", |
|
|
4683 |
"original_ngtdm_Contrast 0.12303268488909444\n", |
|
|
4684 |
"original_ngtdm_Strength 2.2036168675802706e-05\n", |
|
|
4685 |
"wavelet-LLL (92, 92, 92)\n" |
|
|
4686 |
], |
|
|
4687 |
"name": "stdout" |
|
|
4688 |
}, |
|
|
4689 |
{ |
|
|
4690 |
"output_type": "stream", |
|
|
4691 |
"text": [ |
|
|
4692 |
"GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated\n" |
|
|
4693 |
], |
|
|
4694 |
"name": "stderr" |
|
|
4695 |
}, |
|
|
4696 |
{ |
|
|
4697 |
"output_type": "stream", |
|
|
4698 |
"text": [ |
|
|
4699 |
"diagnostics_Versions_PyRadiomics v3.0\n", |
|
|
4700 |
"diagnostics_Versions_Numpy 1.18.1\n", |
|
|
4701 |
"diagnostics_Versions_SimpleITK 1.2.4\n", |
|
|
4702 |
"diagnostics_Versions_PyWavelet 1.0.0\n", |
|
|
4703 |
"diagnostics_Versions_Python 3.6.9\n", |
|
|
4704 |
"diagnostics_Configuration_Settings {'minimumROIDimensions': 2, 'minimumROISize': None, 'normalize': False, 'normalizeScale': 1, 'removeOutliers': None, 'resampledPixelSpacing': None, 'interpolator': 'sitkBSpline', 'preCrop': False, 'padDistance': 5, 'distances': [1], 'force2D': False, 'force2Ddimension': 0, 'resegmentRange': None, 'label': 1, 'additionalInfo': True}\n", |
|
|
4705 |
"diagnostics_Configuration_EnabledImageTypes {'Original': {}}\n", |
|
|
4706 |
"diagnostics_Image-original_Hash 7528be17d60d3bf1b5b0110fb5a17464c0853618\n", |
|
|
4707 |
"diagnostics_Image-original_Dimensionality 3D\n", |
|
|
4708 |
"diagnostics_Image-original_Spacing (1.0, 1.0, 1.0)\n", |
|
|
4709 |
"diagnostics_Image-original_Size (92, 92, 92)\n", |
|
|
4710 |
"diagnostics_Image-original_Mean -388.6534371965271\n", |
|
|
4711 |
"diagnostics_Image-original_Minimum -3102.571326141787\n", |
|
|
4712 |
"diagnostics_Image-original_Maximum 3667.737244068142\n", |
|
|
4713 |
"diagnostics_Mask-original_Hash acd42c92421d3a9df6f4cb58f1548237e3807fb3\n", |
|
|
4714 |
"diagnostics_Mask-original_Spacing (1.0, 1.0, 1.0)\n", |
|
|
4715 |
"diagnostics_Mask-original_Size (92, 92, 92)\n", |
|
|
4716 |
"diagnostics_Mask-original_BoundingBox (3, 1, 7, 85, 89, 78)\n", |
|
|
4717 |
"diagnostics_Mask-original_VoxelNum 184422\n", |
|
|
4718 |
"diagnostics_Mask-original_VolumeNum 1\n", |
|
|
4719 |
"diagnostics_Mask-original_CenterOfMassIndex (42.265206970968755, 46.79903156890176, 48.9044636757003)\n", |
|
|
4720 |
"diagnostics_Mask-original_CenterOfMass (42.265206970968755, 46.79903156890176, 48.9044636757003)\n", |
|
|
4721 |
"original_shape_Elongation 0.8459346778065613\n", |
|
|
4722 |
"original_shape_Flatness 0.6621105014018883\n", |
|
|
4723 |
"original_shape_LeastAxisLength 53.515666406965074\n", |
|
|
4724 |
"original_shape_MajorAxisLength 80.82588373640986\n", |
|
|
4725 |
"original_shape_Maximum2DDiameterColumn 83.81527307120105\n", |
|
|
4726 |
"original_shape_Maximum2DDiameterRow 94.82615672903758\n", |
|
|
4727 |
"original_shape_Maximum2DDiameterSlice 91.67878707749138\n", |
|
|
4728 |
"original_shape_Maximum3DDiameter 100.12991560967181\n", |
|
|
4729 |
"original_shape_MeshVolume 184317.125\n", |
|
|
4730 |
"original_shape_MinorAxisLength 68.37341791699046\n", |
|
|
4731 |
"original_shape_Sphericity 0.5997393163148063\n", |
|
|
4732 |
"original_shape_SurfaceArea 26115.523102962834\n", |
|
|
4733 |
"original_shape_SurfaceVolumeRatio 0.1416879907548625\n", |
|
|
4734 |
"original_shape_VoxelVolume 184422.0\n", |
|
|
4735 |
"original_firstorder_10Percentile -58.81511613748053\n", |
|
|
4736 |
"original_firstorder_90Percentile 154.20990107930592\n", |
|
|
4737 |
"original_firstorder_Energy 11062091490.057745\n", |
|
|
4738 |
"original_firstorder_Entropy 4.08274465087958\n", |
|
|
4739 |
"original_firstorder_InterquartileRange 84.37221745371393\n", |
|
|
4740 |
"original_firstorder_Kurtosis 21.041301992624565\n", |
|
|
4741 |
"original_firstorder_Maximum 1824.4211904882648\n", |
|
|
4742 |
"original_firstorder_MeanAbsoluteDeviation 117.91584250096908\n", |
|
|
4743 |
"original_firstorder_Mean 35.46034521842265\n", |
|
|
4744 |
"original_firstorder_Median 77.97360334121697\n", |
|
|
4745 |
"original_firstorder_Minimum -2394.677782151338\n", |
|
|
4746 |
"original_firstorder_Range 4219.098972639603\n", |
|
|
4747 |
"original_firstorder_RobustMeanAbsoluteDeviation 37.37431032464987\n", |
|
|
4748 |
"original_firstorder_RootMeanSquared 244.91323749477735\n", |
|
|
4749 |
"original_firstorder_Skewness -3.3517084559592085\n", |
|
|
4750 |
"original_firstorder_TotalEnergy 11062091490.057745\n", |
|
|
4751 |
"original_firstorder_Uniformity 0.10098040700963577\n", |
|
|
4752 |
"original_firstorder_Variance 58725.057817163484\n", |
|
|
4753 |
"original_glcm_Autocorrelation 9732.359217574121\n", |
|
|
4754 |
"original_glcm_ClusterProminence 1618157.185488573\n", |
|
|
4755 |
"original_glcm_ClusterShade -14138.262353392947\n", |
|
|
4756 |
"original_glcm_ClusterTendency 262.86682028583607\n", |
|
|
4757 |
"original_glcm_Contrast 21.112098130693514\n", |
|
|
4758 |
"original_glcm_Correlation 0.8473805143807939\n", |
|
|
4759 |
"original_glcm_DifferenceAverage 2.3412889580147724\n", |
|
|
4760 |
"original_glcm_DifferenceEntropy 2.7165015555105163\n", |
|
|
4761 |
"original_glcm_DifferenceVariance 15.44797052162564\n", |
|
|
4762 |
"original_glcm_Id 0.50495679517387\n", |
|
|
4763 |
"original_glcm_Idm 0.4512401024341497\n", |
|
|
4764 |
"original_glcm_Idmn 0.9992733452580136\n", |
|
|
4765 |
"original_glcm_Idn 0.9868129927007071\n", |
|
|
4766 |
"original_glcm_Imc1 -0.21351710081274833\n", |
|
|
4767 |
"original_glcm_Imc2 0.8872181376079317\n", |
|
|
4768 |
"original_glcm_InverseVariance 0.4096777840155984\n", |
|
|
4769 |
"original_glcm_JointAverage 98.345865195845\n", |
|
|
4770 |
"original_glcm_JointEnergy 0.020272742066087677\n", |
|
|
4771 |
"original_glcm_JointEntropy 7.0519693891074455\n", |
|
|
4772 |
"original_glcm_MCC 0.8894177847641864\n", |
|
|
4773 |
"original_glcm_MaximumProbability 0.05169033077308503\n", |
|
|
4774 |
"original_glcm_SumAverage 196.69173039169002\n", |
|
|
4775 |
"original_glcm_SumEntropy 4.86015089598718\n", |
|
|
4776 |
"original_glcm_SumSquares 70.99472960413244\n", |
|
|
4777 |
"original_gldm_DependenceEntropy 7.51971859058017\n", |
|
|
4778 |
"original_gldm_DependenceNonUniformity 14245.367092863107\n", |
|
|
4779 |
"original_gldm_DependenceNonUniformityNormalized 0.07724331746138263\n", |
|
|
4780 |
"original_gldm_DependenceVariance 14.241151757190625\n", |
|
|
4781 |
"original_gldm_GrayLevelNonUniformity 18623.008621531055\n", |
|
|
4782 |
"original_gldm_GrayLevelVariance 94.0459482347969\n", |
|
|
4783 |
"original_gldm_HighGrayLevelEmphasis 9681.986623071\n", |
|
|
4784 |
"original_gldm_LargeDependenceEmphasis 58.0311242693388\n", |
|
|
4785 |
"original_gldm_LargeDependenceHighGrayLevelEmphasis 578142.802632007\n", |
|
|
4786 |
"original_gldm_LargeDependenceLowGrayLevelEmphasis 0.005858639428980389\n", |
|
|
4787 |
"original_gldm_LowGrayLevelEmphasis 0.00011848993582928606\n", |
|
|
4788 |
"original_gldm_SmallDependenceEmphasis 0.12324791426510477\n", |
|
|
4789 |
"original_gldm_SmallDependenceHighGrayLevelEmphasis 1017.9910378184863\n", |
|
|
4790 |
"original_gldm_SmallDependenceLowGrayLevelEmphasis 2.7540998359306532e-05\n", |
|
|
4791 |
"original_glrlm_GrayLevelNonUniformity 12989.120893748053\n", |
|
|
4792 |
"original_glrlm_GrayLevelNonUniformityNormalized 0.08957456337127909\n", |
|
|
4793 |
"original_glrlm_GrayLevelVariance 116.49398827830572\n", |
|
|
4794 |
"original_glrlm_HighGrayLevelRunEmphasis 9606.364893717375\n", |
|
|
4795 |
"original_glrlm_LongRunEmphasis 2.142162761448511\n", |
|
|
4796 |
"original_glrlm_LongRunHighGrayLevelEmphasis 20976.7135894634\n", |
|
|
4797 |
"original_glrlm_LongRunLowGrayLevelEmphasis 0.00023879028611763654\n", |
|
|
4798 |
"original_glrlm_LowGrayLevelRunEmphasis 0.00012339924572602124\n", |
|
|
4799 |
"original_glrlm_RunEntropy 5.206117252984992\n", |
|
|
4800 |
"original_glrlm_RunLengthNonUniformity 96977.21100201242\n", |
|
|
4801 |
"original_glrlm_RunLengthNonUniformityNormalized 0.6660684577062382\n", |
|
|
4802 |
"original_glrlm_RunPercentage 0.7839461836273496\n", |
|
|
4803 |
"original_glrlm_RunVariance 0.47838639574054265\n", |
|
|
4804 |
"original_glrlm_ShortRunEmphasis 0.8419918549732167\n", |
|
|
4805 |
"original_glrlm_ShortRunHighGrayLevelEmphasis 8035.815300731246\n", |
|
|
4806 |
"original_glrlm_ShortRunLowGrayLevelEmphasis 0.00010736170827331377\n", |
|
|
4807 |
"original_glszm_GrayLevelNonUniformity 344.91035365073566\n", |
|
|
4808 |
"original_glszm_GrayLevelNonUniformityNormalized 0.01575940572287013\n", |
|
|
4809 |
"original_glszm_GrayLevelVariance 468.57481789301244\n", |
|
|
4810 |
"original_glszm_HighGrayLevelZoneEmphasis 8059.262816412318\n", |
|
|
4811 |
"original_glszm_LargeAreaEmphasis 128437.01261080142\n", |
|
|
4812 |
"original_glszm_LargeAreaHighGrayLevelEmphasis 1282229886.909851\n", |
|
|
4813 |
"original_glszm_LargeAreaLowGrayLevelEmphasis 12.872030624211071\n", |
|
|
4814 |
"original_glszm_LowGrayLevelZoneEmphasis 0.0002342157285844906\n", |
|
|
4815 |
"original_glszm_SizeZoneNonUniformity 9817.00274147857\n", |
|
|
4816 |
"original_glszm_SizeZoneNonUniformityNormalized 0.448551710750186\n", |
|
|
4817 |
"original_glszm_SmallAreaEmphasis 0.6965723957065041\n", |
|
|
4818 |
"original_glszm_SmallAreaHighGrayLevelEmphasis 5267.925724760801\n", |
|
|
4819 |
"original_glszm_SmallAreaLowGrayLevelEmphasis 0.00019377675733738605\n", |
|
|
4820 |
"original_glszm_ZoneEntropy 8.139739695812759\n", |
|
|
4821 |
"original_glszm_ZonePercentage 0.11867347713396449\n", |
|
|
4822 |
"original_glszm_ZoneVariance 128366.00700039463\n", |
|
|
4823 |
"original_ngtdm_Busyness 0.48177090713638093\n", |
|
|
4824 |
"original_ngtdm_Coarseness 7.36720510011836e-05\n", |
|
|
4825 |
"original_ngtdm_Complexity 15232.460121903936\n", |
|
|
4826 |
"original_ngtdm_Contrast 0.009860810750303298\n", |
|
|
4827 |
"original_ngtdm_Strength 3.068674170180409\n" |
|
|
4828 |
], |
|
|
4829 |
"name": "stdout" |
|
|
4830 |
}, |
|
|
4831 |
{ |
|
|
4832 |
"output_type": "display_data", |
|
|
4833 |
"data": { |
|
|
4834 |
"image/png": 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dM6yRbfH4BcXc3TvAKOmaigOJ3msMIoyR2RKKC2IpE53RxgEAkoSkAoKKCOhQv1bVKTGX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hjnsqIQQ2WhF022TjI0RudzkOxYYkEysyo5Y04ZRQhEDCJBKRxqawSG69+jSQvUd9C919ITekUf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\nRU5ErkJggg==\n", |
|
|
4835 |
"text/plain": [ |
|
|
4836 |
"<Figure size 432x288 with 1 Axes>" |
|
|
4837 |
] |
|
|
4838 |
}, |
|
|
4839 |
"metadata": { |
|
|
4840 |
"tags": [] |
|
|
4841 |
} |
|
|
4842 |
} |
|
|
4843 |
] |
|
|
4844 |
}, |
|
|
4845 |
{ |
|
|
4846 |
"cell_type": "markdown", |
|
|
4847 |
"metadata": { |
|
|
4848 |
"id": "nBKbxeVyMIpR", |
|
|
4849 |
"colab_type": "text" |
|
|
4850 |
}, |
|
|
4851 |
"source": [ |
|
|
4852 |
"# Variable selection" |
|
|
4853 |
] |
|
|
4854 |
}, |
|
|
4855 |
{ |
|
|
4856 |
"cell_type": "markdown", |
|
|
4857 |
"metadata": { |
|
|
4858 |
"id": "EcRDqPYTePa9", |
|
|
4859 |
"colab_type": "text" |
|
|
4860 |
}, |
|
|
4861 |
"source": [ |
|
|
4862 |
" ## LASSO\n", |
|
|
4863 |
"\n", |
|
|
4864 |
" See https://web.stanford.edu/~hastie/glmnet/glmnet_alpha.html#cox.\n", |
|
|
4865 |
" Features are extracted in using the notebook ``lasso/FeatureSelectionLasso.ipynb``." |
|
|
4866 |
] |
|
|
4867 |
}, |
|
|
4868 |
{ |
|
|
4869 |
"cell_type": "code", |
|
|
4870 |
"metadata": { |
|
|
4871 |
"id": "zoqET-Ij_uOI", |
|
|
4872 |
"colab_type": "code", |
|
|
4873 |
"colab": {} |
|
|
4874 |
}, |
|
|
4875 |
"source": [ |
|
|
4876 |
"input_train, output_train, input_test = load_owkin_data()\n", |
|
|
4877 |
"std_input_train, std_input_test = normalizing_input(input_train, input_test)\n", |
|
|
4878 |
"std_input_train.to_csv('MergedStdTrainingInput.csv')" |
|
|
4879 |
], |
|
|
4880 |
"execution_count": 0, |
|
|
4881 |
"outputs": [] |
|
|
4882 |
}, |
|
|
4883 |
{ |
|
|
4884 |
"cell_type": "code", |
|
|
4885 |
"metadata": { |
|
|
4886 |
"id": "32h74yGzX1zG", |
|
|
4887 |
"colab_type": "code", |
|
|
4888 |
"colab": {} |
|
|
4889 |
}, |
|
|
4890 |
"source": [ |
|
|
4891 |
"# Variables \n", |
|
|
4892 |
"\n", |
|
|
4893 |
"lasso_features_lamb_lse = ['original_shape_Maximum3DDiameter',\n", |
|
|
4894 |
" 'original_shape_VoxelVolume',\n", |
|
|
4895 |
" 'original_firstorder_Maximum',\n", |
|
|
4896 |
" 'original_firstorder_Mean',\n", |
|
|
4897 |
" 'original_glcm_ClusterProminence',\n", |
|
|
4898 |
" 'original_glrlm_GrayLevelNonUniformity',\n", |
|
|
4899 |
" 'original_glrlm_RunPercentage',\n", |
|
|
4900 |
" 'Mstage',\n", |
|
|
4901 |
" 'Nstage',\n", |
|
|
4902 |
" 'SourceDataset',\n", |
|
|
4903 |
" 'age',\n", |
|
|
4904 |
" 'Histology_nos']\n", |
|
|
4905 |
"\n", |
|
|
4906 |
"lasso_features_lamb_min = ['original_shape_Maximum3DDiameter',\n", |
|
|
4907 |
" 'original_shape_VoxelVolume',\n", |
|
|
4908 |
" 'original_firstorder_Maximum',\n", |
|
|
4909 |
" 'original_firstorder_Mean',\n", |
|
|
4910 |
" 'original_glcm_ClusterProminence',\n", |
|
|
4911 |
" 'original_glrlm_GrayLevelNonUniformity',\n", |
|
|
4912 |
" 'original_glrlm_RunPercentage',\n", |
|
|
4913 |
" 'Mstage',\n", |
|
|
4914 |
" 'Nstage',\n", |
|
|
4915 |
" 'SourceDataset',\n", |
|
|
4916 |
" 'age',\n", |
|
|
4917 |
" 'Histology_nos']\n", |
|
|
4918 |
"\n", |
|
|
4919 |
"lasso_features_fabrice = ['Mstage',\n", |
|
|
4920 |
" 'Nstage',\n", |
|
|
4921 |
" 'SourceDataset',\n", |
|
|
4922 |
" 'age',\n", |
|
|
4923 |
" 'original_shape_VoxelVolume',\n", |
|
|
4924 |
" 'original_firstorder_Maximum',\n", |
|
|
4925 |
" 'original_firstorder_Mean',\n", |
|
|
4926 |
" 'original_glcm_ClusterProminence',\n", |
|
|
4927 |
" 'original_glcm_Idm',\n", |
|
|
4928 |
" 'original_glcm_Idn',\n", |
|
|
4929 |
" 'original_glrlm_RunPercentage'] " |
|
|
4930 |
], |
|
|
4931 |
"execution_count": 0, |
|
|
4932 |
"outputs": [] |
|
|
4933 |
}, |
|
|
4934 |
{ |
|
|
4935 |
"cell_type": "code", |
|
|
4936 |
"metadata": { |
|
|
4937 |
"id": "4oJZiqYomTx1", |
|
|
4938 |
"colab_type": "code", |
|
|
4939 |
"colab": {} |
|
|
4940 |
}, |
|
|
4941 |
"source": [ |
|
|
4942 |
"input = \"original_shape_Maximum3DDiameter''original_shape_VoxelVolume''original_firstorder_Maximum''original_firstorder_Mean''original_glcm_ClusterProminence''original_glrlm_GrayLevelNonUniformity''original_glrlm_RunPercentage''Mstage''Nstage''SourceDataset''age''Histology_nos\"\n", |
|
|
4943 |
"features = input.split('\\'\\'')\n", |
|
|
4944 |
"features" |
|
|
4945 |
], |
|
|
4946 |
"execution_count": 0, |
|
|
4947 |
"outputs": [] |
|
|
4948 |
}, |
|
|
4949 |
{ |
|
|
4950 |
"cell_type": "markdown", |
|
|
4951 |
"metadata": { |
|
|
4952 |
"id": "7HwOUIJzeSHw", |
|
|
4953 |
"colab_type": "text" |
|
|
4954 |
}, |
|
|
4955 |
"source": [ |
|
|
4956 |
"## Cross correlation" |
|
|
4957 |
] |
|
|
4958 |
}, |
|
|
4959 |
{ |
|
|
4960 |
"cell_type": "markdown", |
|
|
4961 |
"metadata": { |
|
|
4962 |
"id": "PmE1qhmeeJp3", |
|
|
4963 |
"colab_type": "text" |
|
|
4964 |
}, |
|
|
4965 |
"source": [ |
|
|
4966 |
"# Random surivival forest" |
|
|
4967 |
] |
|
|
4968 |
}, |
|
|
4969 |
{ |
|
|
4970 |
"cell_type": "code", |
|
|
4971 |
"metadata": { |
|
|
4972 |
"id": "2kYjuIzieuZZ", |
|
|
4973 |
"colab_type": "code", |
|
|
4974 |
"colab": {} |
|
|
4975 |
}, |
|
|
4976 |
"source": [ |
|
|
4977 |
"!pip install scikit-survival\n", |
|
|
4978 |
"from sksurv.ensemble import RandomSurvivalForest\n", |
|
|
4979 |
"from sklearn.model_selection import cross_validate" |
|
|
4980 |
], |
|
|
4981 |
"execution_count": 0, |
|
|
4982 |
"outputs": [] |
|
|
4983 |
}, |
|
|
4984 |
{ |
|
|
4985 |
"cell_type": "code", |
|
|
4986 |
"metadata": { |
|
|
4987 |
"id": "C7QU5wqnee6n", |
|
|
4988 |
"colab_type": "code", |
|
|
4989 |
"colab": {} |
|
|
4990 |
}, |
|
|
4991 |
"source": [ |
|
|
4992 |
"def load_data(features=None):\n", |
|
|
4993 |
" \"\"\" Load data for RSF usage \"\"\"\n", |
|
|
4994 |
" X_df, y_df, _ = load_owkin_data()\n", |
|
|
4995 |
" if features != None:\n", |
|
|
4996 |
" X_df = X_df[features]\n", |
|
|
4997 |
" X = X_df.to_numpy()\n", |
|
|
4998 |
" y = y_dataframe_to_rsf_input(y_df)\n", |
|
|
4999 |
" return X_df, y_df, X, y\n", |
|
|
5000 |
"\n", |
|
|
5001 |
"X_df, y_df, X, y = load_data(lasso_features_bis)\n", |
|
|
5002 |
"feature_name = list(X_df.columns.values)\n", |
|
|
5003 |
"\n", |
|
|
5004 |
"\"\"\"\n", |
|
|
5005 |
"Train model\n", |
|
|
5006 |
"\"\"\"\n", |
|
|
5007 |
"params = {'min_samples_leaf': 1, 'min_samples_split': 10, 'n_estimators': 10}\n", |
|
|
5008 |
"rsf = RandomSurvivalForest(n_estimators = params['n_estimators'],\n", |
|
|
5009 |
" min_samples_split = params['min_samples_split'],\n", |
|
|
5010 |
" min_samples_leaf = params['min_samples_leaf'],\n", |
|
|
5011 |
" max_features=\"sqrt\",\n", |
|
|
5012 |
" n_jobs=-1\n", |
|
|
5013 |
" )\n", |
|
|
5014 |
"cross_validate(rsf, X, y, cv=5)" |
|
|
5015 |
], |
|
|
5016 |
"execution_count": 0, |
|
|
5017 |
"outputs": [] |
|
|
5018 |
}, |
|
|
5019 |
{ |
|
|
5020 |
"cell_type": "markdown", |
|
|
5021 |
"metadata": { |
|
|
5022 |
"id": "snNqNg37eWZd", |
|
|
5023 |
"colab_type": "text" |
|
|
5024 |
}, |
|
|
5025 |
"source": [ |
|
|
5026 |
"# CoxPH" |
|
|
5027 |
] |
|
|
5028 |
}, |
|
|
5029 |
{ |
|
|
5030 |
"cell_type": "code", |
|
|
5031 |
"metadata": { |
|
|
5032 |
"id": "T0E1Oj9tjv0R", |
|
|
5033 |
"colab_type": "code", |
|
|
5034 |
"colab": {} |
|
|
5035 |
}, |
|
|
5036 |
"source": [ |
|
|
5037 |
"# Prediction\n", |
|
|
5038 |
"def predict(model, X, threshold=0.9):\n", |
|
|
5039 |
" prediction = model.predict_survival_function(X)\n", |
|
|
5040 |
" y_pred = []\n", |
|
|
5041 |
" for pred in prediction:\n", |
|
|
5042 |
" time = pred.x\n", |
|
|
5043 |
" survival_prob = pred.y\n", |
|
|
5044 |
" i_pred = 0\n", |
|
|
5045 |
" while i_pred < len(survival_prob) - 1 and survival_prob[i_pred] > threshold:\n", |
|
|
5046 |
" i_pred += 1\n", |
|
|
5047 |
" y_pred.append(time[i_pred])\n", |
|
|
5048 |
" return pd.DataFrame(np.array([[y, np.nan] for y in y_pred]), index=X.index, columns=['SurvivalTime', 'Event'])" |
|
|
5049 |
], |
|
|
5050 |
"execution_count": 0, |
|
|
5051 |
"outputs": [] |
|
|
5052 |
}, |
|
|
5053 |
{ |
|
|
5054 |
"cell_type": "markdown", |
|
|
5055 |
"metadata": { |
|
|
5056 |
"id": "k4f4-xuEeYU7", |
|
|
5057 |
"colab_type": "text" |
|
|
5058 |
}, |
|
|
5059 |
"source": [ |
|
|
5060 |
"## Baseline" |
|
|
5061 |
] |
|
|
5062 |
}, |
|
|
5063 |
{ |
|
|
5064 |
"cell_type": "code", |
|
|
5065 |
"metadata": { |
|
|
5066 |
"id": "S2dMf-ywfWCi", |
|
|
5067 |
"colab_type": "code", |
|
|
5068 |
"colab": {} |
|
|
5069 |
}, |
|
|
5070 |
"source": [ |
|
|
5071 |
"features_baseline = ['original_shape_Sphericity', 'original_shape_SurfaceVolumeRatio',\n", |
|
|
5072 |
" 'original_shape_Maximum3DDiameter', 'original_glcm_JointEntropy', 'original_glcm_Id',\n", |
|
|
5073 |
" 'original_glcm_Idm', 'SourceDataset', 'Nstage']" |
|
|
5074 |
], |
|
|
5075 |
"execution_count": 0, |
|
|
5076 |
"outputs": [] |
|
|
5077 |
}, |
|
|
5078 |
{ |
|
|
5079 |
"cell_type": "code", |
|
|
5080 |
"metadata": { |
|
|
5081 |
"id": "gKUGXNN8CuXo", |
|
|
5082 |
"colab_type": "code", |
|
|
5083 |
"colab": {} |
|
|
5084 |
}, |
|
|
5085 |
"source": [ |
|
|
5086 |
"!pip install scikit-survival\n", |
|
|
5087 |
"from sksurv.linear_model import CoxPHSurvivalAnalysis\n", |
|
|
5088 |
"from sklearn.model_selection import cross_validate, RandomizedSearchCV\n", |
|
|
5089 |
"from sksurv.util import Surv" |
|
|
5090 |
], |
|
|
5091 |
"execution_count": 0, |
|
|
5092 |
"outputs": [] |
|
|
5093 |
}, |
|
|
5094 |
{ |
|
|
5095 |
"cell_type": "code", |
|
|
5096 |
"metadata": { |
|
|
5097 |
"id": "yv6zkCI2fqQZ", |
|
|
5098 |
"colab_type": "code", |
|
|
5099 |
"colab": {} |
|
|
5100 |
}, |
|
|
5101 |
"source": [ |
|
|
5102 |
"\"\"\" Select features \"\"\"\n", |
|
|
5103 |
"features = lasso_features_lamb_lse\n", |
|
|
5104 |
"\n", |
|
|
5105 |
"# Read data\n", |
|
|
5106 |
"input_train, output_train, input_test = load_owkin_data()\n", |
|
|
5107 |
"input_train = input_train[features]\n", |
|
|
5108 |
"input_test = input_test[features]\n", |
|
|
5109 |
"input_train, input_test = normalizing_input(input_train, input_test)\n", |
|
|
5110 |
"structured_y = Surv.from_dataframe('Event', 'SurvivalTime', output_train)\n", |
|
|
5111 |
"\n", |
|
|
5112 |
"# Grid search\n", |
|
|
5113 |
"tuned_params = {\"alpha\": np.linspace(0.5, 4, 1000)\n", |
|
|
5114 |
" }\n", |
|
|
5115 |
"grid_search = RandomizedSearchCV(CoxPHSurvivalAnalysis(), tuned_params, cv=5, n_jobs=-1, n_iter=500)\n", |
|
|
5116 |
"grid_search.fit(input_train, structured_y)\n", |
|
|
5117 |
"print(grid_search.best_score_)\n", |
|
|
5118 |
"best_params = grid_search.best_params_\n", |
|
|
5119 |
"print(best_params)" |
|
|
5120 |
], |
|
|
5121 |
"execution_count": 0, |
|
|
5122 |
"outputs": [] |
|
|
5123 |
}, |
|
|
5124 |
{ |
|
|
5125 |
"cell_type": "markdown", |
|
|
5126 |
"metadata": { |
|
|
5127 |
"id": "2TKzalaoeaPy", |
|
|
5128 |
"colab_type": "text" |
|
|
5129 |
}, |
|
|
5130 |
"source": [ |
|
|
5131 |
"## Coxnet" |
|
|
5132 |
] |
|
|
5133 |
}, |
|
|
5134 |
{ |
|
|
5135 |
"cell_type": "code", |
|
|
5136 |
"metadata": { |
|
|
5137 |
"id": "TwRqdT5MkGLL", |
|
|
5138 |
"colab_type": "code", |
|
|
5139 |
"colab": {} |
|
|
5140 |
}, |
|
|
5141 |
"source": [ |
|
|
5142 |
"from sksurv.linear_model import CoxnetSurvivalAnalysis\n", |
|
|
5143 |
"from sklearn.model_selection import cross_validate, RandomizedSearchCV\n", |
|
|
5144 |
"from sksurv.util import Surv" |
|
|
5145 |
], |
|
|
5146 |
"execution_count": 0, |
|
|
5147 |
"outputs": [] |
|
|
5148 |
}, |
|
|
5149 |
{ |
|
|
5150 |
"cell_type": "code", |
|
|
5151 |
"metadata": { |
|
|
5152 |
"id": "SoNggMuAjptq", |
|
|
5153 |
"colab_type": "code", |
|
|
5154 |
"colab": {} |
|
|
5155 |
}, |
|
|
5156 |
"source": [ |
|
|
5157 |
"\"\"\" Select features \"\"\"\n", |
|
|
5158 |
"features = high_importance_features_save\n", |
|
|
5159 |
"\n", |
|
|
5160 |
"# Read data\n", |
|
|
5161 |
"input_train, output_train, input_test = load_owkin_data()\n", |
|
|
5162 |
"input_train = input_train[features]\n", |
|
|
5163 |
"input_test = input_test[features]\n", |
|
|
5164 |
"input_train, input_test = normalizing_input(input_train, input_test)\n", |
|
|
5165 |
"structured_y = Surv.from_dataframe('Event', 'SurvivalTime', output_train)\n", |
|
|
5166 |
"\n", |
|
|
5167 |
"# Grid search\n", |
|
|
5168 |
"tuned_params = {\"l1_ratio\": np.linspace(0.0027, 0.0028, 1000),\n", |
|
|
5169 |
" \"n_alphas\": range(65, 75, 1),\n", |
|
|
5170 |
" }\n", |
|
|
5171 |
"grid_search = RandomizedSearchCV(CoxnetSurvivalAnalysis(), tuned_params, cv=5, n_jobs=-1, n_iter=500)\n", |
|
|
5172 |
"grid_search.fit(input_train, structured_y)\n", |
|
|
5173 |
"print(grid_search.best_score_)\n", |
|
|
5174 |
"best_params = grid_search.best_params_\n", |
|
|
5175 |
"print(best_params)" |
|
|
5176 |
], |
|
|
5177 |
"execution_count": 0, |
|
|
5178 |
"outputs": [] |
|
|
5179 |
}, |
|
|
5180 |
{ |
|
|
5181 |
"cell_type": "code", |
|
|
5182 |
"metadata": { |
|
|
5183 |
"id": "qHLKtGZPj_00", |
|
|
5184 |
"colab_type": "code", |
|
|
5185 |
"colab": {} |
|
|
5186 |
}, |
|
|
5187 |
"source": [ |
|
|
5188 |
"# Prediction\n", |
|
|
5189 |
"def predict(model, X, threshold=0.9):\n", |
|
|
5190 |
" prediction = model.predict_survival_function(X)\n", |
|
|
5191 |
" y_pred = []\n", |
|
|
5192 |
" for pred in prediction:\n", |
|
|
5193 |
" time = pred.x\n", |
|
|
5194 |
" survival_prob = pred.y\n", |
|
|
5195 |
" i_pred = 0\n", |
|
|
5196 |
" while i_pred < len(survival_prob) - 1 and survival_prob[i_pred] > threshold:\n", |
|
|
5197 |
" i_pred += 1\n", |
|
|
5198 |
" y_pred.append(time[i_pred])\n", |
|
|
5199 |
" return pd.DataFrame(np.array([[y, np.nan] for y in y_pred]), index=X.index, columns=['SurvivalTime', 'Event'])\n", |
|
|
5200 |
"\n", |
|
|
5201 |
"coxph = CoxnetSurvivalAnalysis(**best_params, fit_baseline_model=True)\n", |
|
|
5202 |
"coxph.fit(input_train, structured_y)\n", |
|
|
5203 |
"y_pred = predict(coxph, input_test)\n", |
|
|
5204 |
"y_pred.to_csv('submission.csv')" |
|
|
5205 |
], |
|
|
5206 |
"execution_count": 0, |
|
|
5207 |
"outputs": [] |
|
|
5208 |
}, |
|
|
5209 |
{ |
|
|
5210 |
"cell_type": "markdown", |
|
|
5211 |
"metadata": { |
|
|
5212 |
"id": "UpruQnDZXc4u", |
|
|
5213 |
"colab_type": "text" |
|
|
5214 |
}, |
|
|
5215 |
"source": [ |
|
|
5216 |
"# AutoML" |
|
|
5217 |
] |
|
|
5218 |
}, |
|
|
5219 |
{ |
|
|
5220 |
"cell_type": "code", |
|
|
5221 |
"metadata": { |
|
|
5222 |
"id": "R48O-V4HYmi3", |
|
|
5223 |
"colab_type": "code", |
|
|
5224 |
"colab": {} |
|
|
5225 |
}, |
|
|
5226 |
"source": [ |
|
|
5227 |
"!sudo apt-get install build-essential swig\n", |
|
|
5228 |
"!pip install auto-sklearn" |
|
|
5229 |
], |
|
|
5230 |
"execution_count": 0, |
|
|
5231 |
"outputs": [] |
|
|
5232 |
}, |
|
|
5233 |
{ |
|
|
5234 |
"cell_type": "code", |
|
|
5235 |
"metadata": { |
|
|
5236 |
"id": "O-N3j3J7Z-qh", |
|
|
5237 |
"colab_type": "code", |
|
|
5238 |
"colab": {} |
|
|
5239 |
}, |
|
|
5240 |
"source": [ |
|
|
5241 |
"import autosklearn.regression" |
|
|
5242 |
], |
|
|
5243 |
"execution_count": 0, |
|
|
5244 |
"outputs": [] |
|
|
5245 |
}, |
|
|
5246 |
{ |
|
|
5247 |
"cell_type": "code", |
|
|
5248 |
"metadata": { |
|
|
5249 |
"id": "EL1SS7JrYJba", |
|
|
5250 |
"colab_type": "code", |
|
|
5251 |
"colab": {} |
|
|
5252 |
}, |
|
|
5253 |
"source": [ |
|
|
5254 |
"from sklearn.model_selection import ShuffleSplit\n", |
|
|
5255 |
"from sklearn.model_selection._split import BaseShuffleSplit, _validate_shuffle_split\n", |
|
|
5256 |
"from sklearn.utils import check_random_state\n", |
|
|
5257 |
"from sklearn.utils.validation import _num_samples " |
|
|
5258 |
], |
|
|
5259 |
"execution_count": 0, |
|
|
5260 |
"outputs": [] |
|
|
5261 |
}, |
|
|
5262 |
{ |
|
|
5263 |
"cell_type": "code", |
|
|
5264 |
"metadata": { |
|
|
5265 |
"id": "RZR5-L6IYhHD", |
|
|
5266 |
"colab_type": "code", |
|
|
5267 |
"colab": {} |
|
|
5268 |
}, |
|
|
5269 |
"source": [ |
|
|
5270 |
"random_state = 42\n", |
|
|
5271 |
"val_size = 0.2\n", |
|
|
5272 |
"n_splits = 1\n", |
|
|
5273 |
"time_left_for_this_task = 600 #60 #600 # default : 3600 # in seconds\n", |
|
|
5274 |
"per_run_time_limit = 30 #30 # default : 360 # in seconds" |
|
|
5275 |
], |
|
|
5276 |
"execution_count": 0, |
|
|
5277 |
"outputs": [] |
|
|
5278 |
}, |
|
|
5279 |
{ |
|
|
5280 |
"cell_type": "code", |
|
|
5281 |
"metadata": { |
|
|
5282 |
"id": "xKKuxBjga-oV", |
|
|
5283 |
"colab_type": "code", |
|
|
5284 |
"colab": {} |
|
|
5285 |
}, |
|
|
5286 |
"source": [ |
|
|
5287 |
"\"\"\" Choose features for Autosklearn \"\"\"\n", |
|
|
5288 |
"feat_ = all_features\n", |
|
|
5289 |
"\n", |
|
|
5290 |
"input_train, output_train, input_test = load_owkin_data()\n", |
|
|
5291 |
"input_train, input_test = normalizing_input(input_train, input_test)\n", |
|
|
5292 |
"input_train = input_train[feat_]\n", |
|
|
5293 |
"input_test = input_test[feat_]" |
|
|
5294 |
], |
|
|
5295 |
"execution_count": 0, |
|
|
5296 |
"outputs": [] |
|
|
5297 |
}, |
|
|
5298 |
{ |
|
|
5299 |
"cell_type": "code", |
|
|
5300 |
"metadata": { |
|
|
5301 |
"id": "_O22QceTa2-h", |
|
|
5302 |
"colab_type": "code", |
|
|
5303 |
"colab": {} |
|
|
5304 |
}, |
|
|
5305 |
"source": [ |
|
|
5306 |
"class ShuffleSplit(BaseShuffleSplit):\n", |
|
|
5307 |
" \n", |
|
|
5308 |
" def __init__(self, n_splits=10, test_size=None, train_size=None,\n", |
|
|
5309 |
" random_state=None):\n", |
|
|
5310 |
" n_splits = 1 # !!!!!!!!!!!!!!!!!!!!!!!!!!!!! PROBLEM : n_splits stays at 10 even when changing default value\n", |
|
|
5311 |
"\n", |
|
|
5312 |
" super().__init__(\n", |
|
|
5313 |
" n_splits=n_splits,\n", |
|
|
5314 |
" test_size=test_size,\n", |
|
|
5315 |
" train_size=train_size,\n", |
|
|
5316 |
" random_state=random_state)\n", |
|
|
5317 |
" self._default_test_size = 0.1\n", |
|
|
5318 |
"\n", |
|
|
5319 |
" def _iter_indices(self, X, y=None, groups=None):\n", |
|
|
5320 |
" n_samples = _num_samples(X)\n", |
|
|
5321 |
" n_train, n_test = _validate_shuffle_split(\n", |
|
|
5322 |
" n_samples, self.test_size, self.train_size,\n", |
|
|
5323 |
" default_test_size=self._default_test_size)\n", |
|
|
5324 |
"\n", |
|
|
5325 |
" rng = check_random_state(self.random_state)\n", |
|
|
5326 |
" for i in range(self.n_splits):\n", |
|
|
5327 |
" # random partition\n", |
|
|
5328 |
" permutation = rng.permutation(n_samples)\n", |
|
|
5329 |
" ind_test = permutation[:n_test]\n", |
|
|
5330 |
" ind_train = permutation[n_test:(n_test + n_train)]\n", |
|
|
5331 |
" yield ind_train, ind_test" |
|
|
5332 |
], |
|
|
5333 |
"execution_count": 0, |
|
|
5334 |
"outputs": [] |
|
|
5335 |
}, |
|
|
5336 |
{ |
|
|
5337 |
"cell_type": "code", |
|
|
5338 |
"metadata": { |
|
|
5339 |
"id": "_U9BIzsta4OH", |
|
|
5340 |
"colab_type": "code", |
|
|
5341 |
"colab": {} |
|
|
5342 |
}, |
|
|
5343 |
"source": [ |
|
|
5344 |
"suffle_split = ShuffleSplit(n_splits=n_splits, test_size=val_size, random_state=random_state)\n", |
|
|
5345 |
"ind_train, ind_val = [_tuple for _tuple in suffle_split._iter_indices(input_train, \n", |
|
|
5346 |
" y=output_train[\"SurvivalTime\"], \n", |
|
|
5347 |
" groups=None)][0]\n", |
|
|
5348 |
"n_train = len(ind_train)\n", |
|
|
5349 |
"n_val = len(ind_val)\n", |
|
|
5350 |
"print(\"n_train\", n_train)\n", |
|
|
5351 |
"print(\"n_val\", n_val)" |
|
|
5352 |
], |
|
|
5353 |
"execution_count": 0, |
|
|
5354 |
"outputs": [] |
|
|
5355 |
}, |
|
|
5356 |
{ |
|
|
5357 |
"cell_type": "code", |
|
|
5358 |
"metadata": { |
|
|
5359 |
"id": "iqLRrqxVYlQ0", |
|
|
5360 |
"colab_type": "code", |
|
|
5361 |
"colab": {} |
|
|
5362 |
}, |
|
|
5363 |
"source": [ |
|
|
5364 |
"automl = autosklearn.regression.AutoSklearnRegressor(time_left_for_this_task=time_left_for_this_task, # in seconds\n", |
|
|
5365 |
" per_run_time_limit=per_run_time_limit,\n", |
|
|
5366 |
" resampling_strategy=ShuffleSplit,\n", |
|
|
5367 |
" resampling_strategy_arguments={'n_splits': n_splits,\n", |
|
|
5368 |
" 'test_size': val_size,\n", |
|
|
5369 |
" 'random_state': random_state})" |
|
|
5370 |
], |
|
|
5371 |
"execution_count": 0, |
|
|
5372 |
"outputs": [] |
|
|
5373 |
}, |
|
|
5374 |
{ |
|
|
5375 |
"cell_type": "code", |
|
|
5376 |
"metadata": { |
|
|
5377 |
"id": "_SrMT4fSaA7l", |
|
|
5378 |
"colab_type": "code", |
|
|
5379 |
"colab": {} |
|
|
5380 |
}, |
|
|
5381 |
"source": [ |
|
|
5382 |
"available_models = [model_name for model_name in autosklearn.pipeline.components.regression._regressors]\n", |
|
|
5383 |
"print(\"Available models in AutoSklearnRegressor : \")\n", |
|
|
5384 |
"available_models" |
|
|
5385 |
], |
|
|
5386 |
"execution_count": 0, |
|
|
5387 |
"outputs": [] |
|
|
5388 |
}, |
|
|
5389 |
{ |
|
|
5390 |
"cell_type": "code", |
|
|
5391 |
"metadata": { |
|
|
5392 |
"id": "UU8x0IyVaJxF", |
|
|
5393 |
"colab_type": "code", |
|
|
5394 |
"colab": {} |
|
|
5395 |
}, |
|
|
5396 |
"source": [ |
|
|
5397 |
"def my_cindex(y_true, y_pred, patient_ids, events, ind_train, ind_val, n_train, n_val, print_cindex=True):\n", |
|
|
5398 |
" y_true = y_true.ravel()\n", |
|
|
5399 |
" y_pred = y_pred.ravel()\n", |
|
|
5400 |
" \n", |
|
|
5401 |
" if len(y_true)==n_train:\n", |
|
|
5402 |
" ind = ind_train\n", |
|
|
5403 |
" elif len(y_true)==n_val:\n", |
|
|
5404 |
" ind = ind_val\n", |
|
|
5405 |
" else:\n", |
|
|
5406 |
" raise Exception(\"y_true.shape={} but should be either {} or {}\".format(y_true.shape, n_train, n_val))\n", |
|
|
5407 |
"\n", |
|
|
5408 |
" df_y_true = pd.DataFrame({\"PatientID\": patient_ids[ind],\n", |
|
|
5409 |
" \"SurvivalTime\": y_true,\n", |
|
|
5410 |
" \"Event\": events[ind]\n", |
|
|
5411 |
" })\n", |
|
|
5412 |
" df_y_true = df_y_true.set_index(\"PatientID\")\n", |
|
|
5413 |
"\n", |
|
|
5414 |
" df_y_pred = pd.DataFrame({\"PatientID\": patient_ids[ind],\n", |
|
|
5415 |
" \"SurvivalTime\": y_pred,\n", |
|
|
5416 |
" \"Event\": events[ind]*np.nan # because \"`Event`column, whose value does not matter\" in metrics_t9gbvr2.py\n", |
|
|
5417 |
" })\n", |
|
|
5418 |
" df_y_pred = df_y_pred.set_index(\"PatientID\")\n", |
|
|
5419 |
" \n", |
|
|
5420 |
" cindex_value = cindex(df_y_true, df_y_pred)\n", |
|
|
5421 |
" \n", |
|
|
5422 |
" if print_cindex:\n", |
|
|
5423 |
" if len(y_true)==n_train:\n", |
|
|
5424 |
" print(\"train_cindex\", cindex_value)\n", |
|
|
5425 |
" else:\n", |
|
|
5426 |
" print(\"val_cindex\", cindex_value)\n", |
|
|
5427 |
" \n", |
|
|
5428 |
" return cindex_value\n", |
|
|
5429 |
"\n", |
|
|
5430 |
"\n", |
|
|
5431 |
"cindex_scorer = autosklearn.metrics.make_scorer(name=\"cindex\",\n", |
|
|
5432 |
" score_func=my_cindex,\n", |
|
|
5433 |
" optimum=1,\n", |
|
|
5434 |
" greater_is_better=True,\n", |
|
|
5435 |
" needs_proba=False,\n", |
|
|
5436 |
" needs_threshold=False,\n", |
|
|
5437 |
" patient_ids = output_train.index.values,\n", |
|
|
5438 |
" events = output_train[\"Event\"].values,\n", |
|
|
5439 |
" ind_train = ind_train,\n", |
|
|
5440 |
" ind_val = ind_val,\n", |
|
|
5441 |
" n_train = n_train,\n", |
|
|
5442 |
" n_val = n_val,\n", |
|
|
5443 |
" print_cindex=False)" |
|
|
5444 |
], |
|
|
5445 |
"execution_count": 0, |
|
|
5446 |
"outputs": [] |
|
|
5447 |
}, |
|
|
5448 |
{ |
|
|
5449 |
"cell_type": "code", |
|
|
5450 |
"metadata": { |
|
|
5451 |
"id": "UWLNMLjHaExs", |
|
|
5452 |
"colab_type": "code", |
|
|
5453 |
"colab": {} |
|
|
5454 |
}, |
|
|
5455 |
"source": [ |
|
|
5456 |
"import time\n", |
|
|
5457 |
"start_time = time.time()\n", |
|
|
5458 |
"automl.fit(input_train.copy(), \n", |
|
|
5459 |
" output_train[\"SurvivalTime\"].copy(),\n", |
|
|
5460 |
" metric=cindex_scorer)\n", |
|
|
5461 |
"execution_time = time.time()-start_time" |
|
|
5462 |
], |
|
|
5463 |
"execution_count": 0, |
|
|
5464 |
"outputs": [] |
|
|
5465 |
}, |
|
|
5466 |
{ |
|
|
5467 |
"cell_type": "code", |
|
|
5468 |
"metadata": { |
|
|
5469 |
"id": "0Jl_Nck-deav", |
|
|
5470 |
"colab_type": "code", |
|
|
5471 |
"colab": {} |
|
|
5472 |
}, |
|
|
5473 |
"source": [ |
|
|
5474 |
"execution_time" |
|
|
5475 |
], |
|
|
5476 |
"execution_count": 0, |
|
|
5477 |
"outputs": [] |
|
|
5478 |
}, |
|
|
5479 |
{ |
|
|
5480 |
"cell_type": "code", |
|
|
5481 |
"metadata": { |
|
|
5482 |
"id": "QHgiub_Sdh8T", |
|
|
5483 |
"colab_type": "code", |
|
|
5484 |
"colab": {} |
|
|
5485 |
}, |
|
|
5486 |
"source": [ |
|
|
5487 |
"print(automl.sprint_statistics())" |
|
|
5488 |
], |
|
|
5489 |
"execution_count": 0, |
|
|
5490 |
"outputs": [] |
|
|
5491 |
}, |
|
|
5492 |
{ |
|
|
5493 |
"cell_type": "code", |
|
|
5494 |
"metadata": { |
|
|
5495 |
"id": "zdHztXV_cU-L", |
|
|
5496 |
"colab_type": "code", |
|
|
5497 |
"colab": {} |
|
|
5498 |
}, |
|
|
5499 |
"source": [ |
|
|
5500 |
"start_time = time.time()\n", |
|
|
5501 |
"automl.refit(input_train.iloc[ind_train].copy(), output_train.iloc[ind_train][\"SurvivalTime\"].copy())\n", |
|
|
5502 |
"execution_time = time.time()-start_time\n", |
|
|
5503 |
"print(\"execution_time\", execution_time)" |
|
|
5504 |
], |
|
|
5505 |
"execution_count": 0, |
|
|
5506 |
"outputs": [] |
|
|
5507 |
}, |
|
|
5508 |
{ |
|
|
5509 |
"cell_type": "code", |
|
|
5510 |
"metadata": { |
|
|
5511 |
"id": "VngBx3HuceFf", |
|
|
5512 |
"colab_type": "code", |
|
|
5513 |
"colab": {} |
|
|
5514 |
}, |
|
|
5515 |
"source": [ |
|
|
5516 |
"print(\"resampling_strategy :\", automl.resampling_strategy.__name__)\n", |
|
|
5517 |
"print(automl.sprint_statistics())" |
|
|
5518 |
], |
|
|
5519 |
"execution_count": 0, |
|
|
5520 |
"outputs": [] |
|
|
5521 |
}, |
|
|
5522 |
{ |
|
|
5523 |
"cell_type": "code", |
|
|
5524 |
"metadata": { |
|
|
5525 |
"id": "KjQUqWkNXd4d", |
|
|
5526 |
"colab_type": "code", |
|
|
5527 |
"colab": {} |
|
|
5528 |
}, |
|
|
5529 |
"source": [ |
|
|
5530 |
"y_test_pred = automl.predict(input_test)\n", |
|
|
5531 |
"y_pred_df = pd.DataFrame(np.array([[y, np.nan] for y in y_test_pred]), index=input_test.index, columns=['SurvivalTime', 'Event'])\n", |
|
|
5532 |
"y_pred_df.to_csv('submission.csv')" |
|
|
5533 |
], |
|
|
5534 |
"execution_count": 0, |
|
|
5535 |
"outputs": [] |
|
|
5536 |
}, |
|
|
5537 |
{ |
|
|
5538 |
"cell_type": "code", |
|
|
5539 |
"metadata": { |
|
|
5540 |
"id": "xazNCneScNOV", |
|
|
5541 |
"colab_type": "code", |
|
|
5542 |
"colab": {} |
|
|
5543 |
}, |
|
|
5544 |
"source": [ |
|
|
5545 |
"" |
|
|
5546 |
], |
|
|
5547 |
"execution_count": 0, |
|
|
5548 |
"outputs": [] |
|
|
5549 |
}, |
|
|
5550 |
{ |
|
|
5551 |
"cell_type": "markdown", |
|
|
5552 |
"metadata": { |
|
|
5553 |
"id": "oSR7n7hzeMsb", |
|
|
5554 |
"colab_type": "text" |
|
|
5555 |
}, |
|
|
5556 |
"source": [ |
|
|
5557 |
"End" |
|
|
5558 |
] |
|
|
5559 |
} |
|
|
5560 |
] |
|
|
5561 |
} |